Systems and methods of enhancing biometric matching accuracy and/or efficiency

ABSTRACT

Exemplary embodiments are directed to a system of enhancing biometric analysis matching. A processing device is configured to analyze the probe short-range and broadband iris texture information of a probe image for iris biometric authenticity, and based on the biometric authenticity of the probe short-range iris texture information and the probe broadband iris texture information, determine the biometric authenticity of the subject. Exemplary embodiments are also directed to a system of enhancing biometric analysis matching efficiency. The processing device generates an optimized order of enrollment iris biometric data includes a listing of the enrollment iris biometric data ordered by closest match to furthest match between the probe and enrollment broadband iris texture information. The processing device analyzes the iris biometric data for biometric authenticity starting with the closest match between the probe and enrollment broadband iris texture information.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application is a continuation application of U.S.Non-Provisional Application No. 16/675,546, filed on Nov. 6, 2019(issued as U.S. Pat. No. 11,157,733 on Oct. 26, 2021), which claims thebenefit of priority to U.S. Provisional Application No. 62/756,138,filed on Nov. 6, 2018, the content of the foregoing patent applicationsis incorporated herein by reference in its entirety.

TECHNICAL FIELD

The present disclosure relates to systems and methods of enhancingbiometric analysis matching accuracy based on short-range and broadbandiris texture and/or enhancing biometric analysis matching efficiencybased on broadband iris texture.

BACKGROUND

Security is a concern in a variety of transactions involving privateinformation. Iris recognition is a well-accepted and accurate means ofbiometric identification used in government and commercial systemsaround the world that enables secure transactions and an added layer ofsecurity beyond keys and/or passwords. Due to the increased securityprovided by iris recognition systems, an increase in use of such systemshas occurred around the world. As the size of databases of enrolledindividuals increases, banks, hospitals, schools, stores, businesses,military installations, and other government and/or commercial systemscould benefit from biometric analysis systems that improve the accuracyand efficiency of biometric verification.

A need exists for improved biometric analysis systems to improve theaccuracy and efficiency of the authentication process. These and otherneeds are addressed by the systems and methods of the presentdisclosure.

SUMMARY

In accordance with embodiments of the present disclosure, an exemplarysystem of enhancing biometric analysis matching accuracy (e.g., a dualmodality biometric analysis system) is provided. The system includes atleast one camera and a processing device in communication with the atleast one camera. The at least one camera can be configured to captureat least one probe image of an iris of a subject. The at least one probeimage can have iris biometric data associated with the iris of thesubject. The iris biometric data can include probe short-range iristexture information and probe broadband iris texture information. Theprocessing device can be configured to receive as input the at least oneprobe image, analyze the probe short-range iris texture information ofthe at least one probe image for iris biometric authenticity, andanalyze the probe broadband iris texture information of the at least oneprobe image for iris biometric authenticity. Based on the biometricauthenticity of the probe short-range iris texture information and theprobe broadband iris texture information, the processing device can beconfigured to determine the biometric authenticity of the subject,thereby preferably enhancing biometric analysis matching accuracythereof.

As used herein, short-range iris texture refers to localized irispatterns or textures that are not human-readable, i.e., that are on alength scale finer than can be resolved by a human observer. Theshort-range iris textures survive filtering with a narrow band filtercentered about a high spatial frequency with a short wavelength. Theshort-range iris textures are in the Daugman spatial frequency range offeatures traditionally used by biometric analysis systems. See, e.g., J.Daugman, “High Confidence Visual Recognition of Persons by A Test ofStatistical Independence”, IEEE Trans. Pattern Anal. MachineIntelligence, vol. 15, No. 11, pp. 1148-1161 (1993). Short-range iristextural features include fine iris features that are within the filterwindow of the Daugman iris-matching algorithm, and can be random with alarge number of degrees of freedom. The short-range iris textures can bedetected and visualized through the phase of a two-dimensional Gaborfilter, and can be on the scale of approximately ±10 pixels, therebylimited to predetermined Gabor wavelets. It is assumed that the irisdiameter is of about 80 pixels to about 400 pixels.

As used herein, broadband iris texture refers to extended iris patternsor textures that form durable, coarse, and human-readable orhuman-identifiable patterns, i.e., patterns that may be visuallyperceptible by a human that is observing the near infrared image of asubject iris. Broadband iris texture typically extends across multiplefrequencies for which constituent lower frequencies may be humanperceptible and constituent higher frequencies might not be visuallyperceptible, i.e., the broadband iris texture information relates to aphysiological feature that may include information at multiple spatialfrequencies of analysis, but does not necessarily do so. The broadbandiris textures survive filtering with a low-pass filter with a cut-off ata low spatial frequency with a long wavelength. The broadband iristextures are detected and visualized generally outside of the Daugmanspatial frequency range. The broadband iris texture can extend overlarge areas of territory of the iris, and can include some frequenciesof the Daugman short-range iris texture. The broadband iris textureexceeds the pixel range of the Gabor filter, and can be one-dimensionalor two-dimensional in nature. The broadband iris texture can bedetermined through human detected, automated and/or machine learningand/or vision, using vision algorithms including filtering or withneural networks. The broadband iris textural features can include, forexample, crypts, sunbursts, branch cracks, rings of dots, flames,Wolfflin nodules, background texture, characteristics of the collarette,white-wall tires, combinations thereof, and other features that show upon near infrared iris images.

In some embodiments, the system can include at least one illuminationsource configured to illuminate an iris of a subject. The processingdevice can be in communication with the at least one illuminationsource. The at least one illumination source can be configured toilluminate the iris of the subject with near infrared light. The atleast one camera can be configured to capture the at least one probeimage of the iris of the subject during illumination of the subject withthe at least one illumination source.

Analyzing the probe short-range iris texture information for biometricauthenticity can include comparing the probe short-range iris textureinformation to enrollment short-range iris texture information.Analyzing the probe broadband iris texture information for biometricauthenticity can include comparing the probe broadband iris textureinformation to enrollment broadband iris texture information. In someembodiments, example systems can compare the probe and enrollmentbroadband iris texture information for detection and determination ofthe existence and/or nature/quality of one or more broadband iristextures in the probe image, rather only specific size, shape, position,or the like, of the broadband iris texture. In some embodiments, it canbe determined whether the broadband iris texture is in a specificposition and/or of a specific size and/or whether the type of broadbandiris texture of the enrollment image is detected in the probe image,thus preferably enhancing biometric analysis matching accuracy. As shallbe discussed further herein, for example, a feature vector associatedwith broadband iris texture can include set members comprisingbackground texture, collaret, crypts, and/or Wolfflin nodules, forexample, and, each can be rated for example, on a scale of zero to three0-3, while each other is rated on another scale.

In some embodiments, the processing device can be configured to applythe biometric authenticity determination based on the probe broadbandiris texture information as a final deciding factor in the biometricauthenticity of the subject. In some embodiments, the at least one probeimage can include iris biometric data associated with left and rightirises of the subject. In such embodiments, the processing device can beconfigured to analyze the probe short-range and broadband iris textureinformation for both the left and right irises of the subject.

In some embodiments, the processing device can be configured to generatea short-range score (e.g., a short-range iris dissimilarity score)corresponding with a degree to which the iris biometric authenticity isfound for the probe short-range iris texture information. If thegenerated score for the iris biometric authenticity of the probeshort-range iris texture information is better than (e.g., above) ashort-range threshold value, the processing device can be configured toanalyze the probe broadband iris texture information of the at least oneprobe image for the iris biometric authenticity. For example, a score ofapproximately 0 can correspond with a perfect match, while a score ofover 0.33 can correspond with a non-match. In some embodiments, a scoreof 0.38 can be considered as the threshold value for a non-match. Insome embodiments, broadband iris texture analysis can be performed ifthe generated short-range score is better than (e.g., above) 0.38. Insome embodiments, the broadband iris texture analysis can be performedif the generated short-range score is greater than 0.2, thus preferablyenhancing biometric analysis matching accuracy.

In some embodiments, the processing device can be configured to generatea broadband score (e.g., broadband iris dissimilarity score)corresponding with a degree to which the iris biometric authenticity isfound for the probe broadband iris texture information. If the generatedbroadband score is better than (e.g., above) a broadband thresholdvalue, positive biometric authenticity of the subject can be determined.

In accordance with embodiments of the present disclosure, an exemplarymethod of enhancing biometric analysis matching is provided. The methodincludes capturing at least one probe image of an iris of a subject withat least one camera. The at least one probe image can have irisbiometric data associated with the iris of the subject. The irisbiometric data can include probe short-range iris texture informationand probe broadband iris texture information. The method includesreceiving as input at a processing device the at least one probe image,analyzing, via the processing device, the probe short-range iris textureinformation of the at least one probe image for iris biometricauthenticity, and analyzing, via the processing device, the probebroadband iris texture information of the at least one probe image foriris biometric authenticity. Based on the biometric authenticity of theprobe short-range iris texture information and the probe broadband iristexture information, the method includes determining the biometricauthenticity of the subject, thus preferably enhancing biometricanalysis matching accuracy.

In some embodiments, the method can include illuminating the iris of thesubject with at least one illumination source. In some embodiments, themethod can include illuminating the iris of the subject with nearinfrared light from the at least one illumination source. In suchembodiments, the method can include capturing the at least one probeimage of the iris of the subject during illumination of the subject withthe at least one illumination source.

Analyzing the probe short-range iris texture information for biometricauthenticity can include comparing the probe short-range iris textureinformation to enrollment short-range iris texture information.Analyzing the probe broadband iris texture information for biometricauthenticity can include comparing the probe broadband iris textureinformation to enrollment broadband iris texture information. In someembodiments, the method can include applying, via the processing device,the biometric authenticity determination based on the probe broadbandiris texture information as a final deciding factor in the biometricauthenticity of the subject.

In some embodiments, the at least one probe image can include irisbiometric data associated with left and right irises of the subject. Insuch embodiments, the method can include analyzing, via the processingdevice, the probe short-range and broadband iris texture information forboth the left and right irises of the subject. For example, rather thanrelying on authentication based on only the short and broadband iristexture information of the left iris of the subject, the short andbroadband iris texture information for both the left and right irisescan be analyzed relative to respective left and right iris enrollmentimages.

In accordance with embodiments of the present disclosure, an exemplarynon-transitory computer-readable medium storing instructions forenhancing biometric analysis matching is provided. The instructions areexecutable by a processing device. Execution of the instructions by theprocessing device causes the processing device to capture at least oneprobe image of an iris of a subject with at least one camera. The atleast one probe image can have iris biometric data associated with theiris of the subject. The iris biometric data can include probeshort-range iris texture information and probe broadband iris textureinformation. Execution of the instructions by the processing devicecauses the processing device to receive as input at a processing devicethe at least one probe image, analyze, via the processing device, theprobe short-range iris texture information of the at least one probeimage for iris biometric authenticity, and analyze, via the processingdevice, the probe broadband iris texture information of the at least oneprobe image for iris biometric authenticity. Based on the biometricauthenticity of the probe short-range iris texture information and theprobe broadband iris texture information, execution of the instructionsby the processing device causes the processing device to determine thebiometric authenticity of the subject, thus preferably enhancingbiometric analysis matching accuracy.

While systems and/or subsystem (and/or methods and/or sub-methods) forenhancing biometric analysis matching accuracy can be provided, systemsand subsystems (and/or methods and/or sub-methods) can be provided forenhancing biometric analysis matching efficiency.

Regarding efficiency, in accordance with some embodiments of the presentdisclosure, an exemplary system enhancing biometric analysis efficiencyis provided. The system includes at least one camera, a database, and aprocessing device in communication with the at least one camera and thedatabase. The at least one camera is configured to capture at least oneprobe image of an iris of a subject. The at least one probe image hasiris biometric data associated with the iris of the subject. The irisbiometric data includes probe short-range iris texture information andprobe broadband iris texture information. The database electronicallystores enrollment iris biometric data including enrollment short-rangeiris texture information and enrollment broadband iris textureinformation.

The processing device is configured to receive as input the at least oneprobe image, and generate an optimized order of the enrollment irisbiometric data based on biometric analysis of the probe broadband iristexture information relative to the enrollment broadband iris textureinformation. The optimized order includes a listing of the enrollmentiris biometric data ordered by closest match to furthest match betweenthe probe and enrollment broadband iris texture information. Theprocessing device is configured to analyze the iris biometric data forbiometric authenticity based on the probe and enrollment short-rangeiris texture information starting with the closest match between theprobe and enrollment broadband iris texture information, therebypreferably enhancing biometric analysis matching efficiently.

In some embodiments, the system can include at least one illuminationsource configured to illuminate an iris of a subject. The processingdevice can be in communication with the at least one illuminationsource. In some embodiments, the at least one illumination source can beconfigured to illuminate the iris of the subject with near infrared(NIR) light. In such embodiments, the at least one camera can beconfigured to capture the at least one probe image of the iris of thesubject during illumination of the subject with the at least oneillumination source.

In some embodiments, biometric analysis of the probe broadband iristexture information relative to the enrollment broadband iris textureinformation can include generating a feature vector for each of theprobe and enrollment broadband iris texture information. As discussedherein, a feature vector can represent the number of broadband iristexture features being analyzed and the magnitude of presence of suchbroadband iris texture features. For example, in feature vector (F₁, F₂,F_(N)), F represents each of broadband iris texture features, and Nrepresents the number of broadband iris texture features. A magnitude ofthe numerical value for each broadband iris texture feature, for example(0, 1, 0) represents the presence or absence of the feature. In the (0,1, 0) example, broadband features F₁ and F₃ are not present, whilefeature F₂ is present. Biometric analysis of the probe broadband iristexture information relative to the enrollment broadband iris textureinformation can include generating a set of distances between thefeature vector of the probe broadband iris texture information and thefeature vector of each of the enrollment broadband iris textureinformation. In some embodiments, the set of distances between thefeature vectors can be defined by a Euclidian distance determination.However, a feature vector can include, for example, four features (F₁,F₂, F₃, F₄) with each on other types of scales, such as where each ofF₁, F₂, F₃, and F₄ can be on a scale of zero to three (as opposed tozero to one in the above example).

The set of distances between the feature vectors can characterize asimilarity between the feature vector of the probe and enrollmentbroadband iris texture information. For example, a small distancebetween the feature vectors of the probe and enrollment broadband iristexture information can correspond with a high or close match betweenthe probe and enrollment broadband iris texture information. Similarly,a large distance between the feature vectors of the probe and enrollmentbroadband iris texture information can correspond with a non-match orfar (e.g., low) match between the probe and enrollment broadband iristexture information.

In some embodiments, analyzing the iris biometric data for biometricauthenticity comprises comparing the probe short-range iris textureinformation to the enrollment short-range iris texture information. Insome embodiments, the at least one probe image can include irisbiometric data associated with left and right irises of the subject. Insuch embodiments, the processing device can be configured to generatethe optimized order of the enrollment iris biometric data for both theleft and right irises of the subject. For example, a first optimizedorder for the left iris enrollment biometric data can be generated, anda second optimized order for the right iris enrollment biometric datacan be generated, such that independent (or simultaneous) matching ofthe left and right iris probe images can be performed to the respectivefirst and second optimized orders.

In accordance with embodiments of the present disclosure, an exemplarymethod of enhancing biometric analysis matching efficiency is provided.The method includes capturing at least one probe image of an iris of asubject with at least one camera. The at least one probe image has irisbiometric data associated with the iris of the subject. The irisbiometric data includes probe short-range iris texture information andprobe broadband iris texture information. The method includes receivingas input at a processing device the at least one probe image, andgenerating, via the processing device, an optimized order of enrollmentiris biometric data electronically stored in a database based onbiometric analysis of the probe broadband iris texture informationrelative to enrollment broadband iris texture information, therebypreferably enhancing biometric analysis matching efficiently. Theoptimized order can include a listing of the enrollment iris biometricdata ordered by closest match to furthest match between the probe andenrollment broadband iris texture information. The method includesanalyzing, via the processing device, the iris biometric data forbiometric authenticity based on the probe short-range iris textureinformation and enrollment short-range iris texture information startingwith the closest match between the probe and enrollment broadbandtexture information.

In some embodiments, the method can include illuminating the iris of thesubject with near infrared light from at least one illumination source.In some embodiments, the method can include generating a feature vectorfor each of the probe and enrollment broadband iris texture information,and generating a set of distances between the feature vector of theprobe broadband iris texture information and the feature vector of eachof the enrollment broadband iris texture information. In suchembodiments, a small distance between the feature vectors of the probeand enrollment broadband iris texture information can correspond with aclose match between the probe and enrollment broadband iris textureinformation, and a large distance between the feature vectors of theprobe and enrollment broadband iris texture information can correspondwith a far (e.g., low) match between the probe and enrollment broadbandiris texture information.

In accordance with embodiments of the present disclosure, an exemplarynon-transitory computer-readable medium storing instructions forenhancing biometric analysis matching efficiency is provided. Theinstructions are executable by a processing device. Execution of theinstructions by the processing device causes the processing device tocapture at least one probe image of an iris of a subject with at leastone camera. The at least one probe image includes iris biometric dataassociated with the iris of the subject. The iris biometric dataincludes probe short-range iris texture information and probe broadbandiris texture information.

Execution of the instructions by the processing device causes theprocessing device to receive as input at a processing device the atleast one probe image, and generate, via the processing device, anoptimized order of enrollment iris biometric data electronically storedin a database based on biometric analysis of the probe broadband iristexture information relative to enrollment broadband iris textureinformation, thereby preferably enhancing biometric analysis matchingefficiently. The optimized order includes a listing of the enrollmentiris biometric data ordered by closest match to furthest match betweenthe probe and enrollment broadband iris texture information. Executionof the instructions by the processing device causes the processingdevice to analyze, via the processing device, the iris biometric datafor biometric authenticity based on the probe short-range iris textureinformation and enrollment short-range iris texture information startingwith the closest match between the probe and enrollment broadbandtexture information.

In accordance with embodiments of the present disclosure, an exemplaryenhanced data storage efficiency system for a computer memory isprovided. The system includes means for capturing at least one probeimage of an iris of a subject. The at least one probe image has irisbiometric data associated with the iris of the subject. The irisbiometric data includes probe short-range iris texture information andprobe broadband iris texture information. The system includes means forgenerating an optimized order of enrollment iris biometric dataelectronically stored in a database based on biometric analysis of theprobe broadband iris texture information relative to enrollmentbroadband iris texture information. The optimized order includes alisting of the enrollment iris biometric data ordered by closest matchto furthest match between the probe and enrollment broadband iristexture information. The method includes means for analyzing the irisbiometric data for biometric authenticity based on the probe short-rangeiris texture information and enrollment short-range iris textureinformation starting with the closest match between the probe andenrollment broadband texture information.

While systems and/or subsystem (and/or methods and/or sub-methods) forenhancing biometric analysis matching accuracy can be provided, andwhile systems and subsystems (and/or methods and/or sub-methods) can beprovided for enhancing biometric analysis matching efficiency, it isalso contemplated that systems and/or subsystem (and/or methods and/orsub-methods) for enhancing both biometric analysis matching accuracy andefficiency can be provided

Other objects and features will become apparent from the followingdetailed description considered in conjunction with the accompanyingdrawings. It is to be understood, however, that the drawings aredesigned as an illustration only and not as a definition of the limitsof the invention.

BRIEF DESCRIPTION OF THE DRAWINGS

To assist those of skill in the art in making and using the disclosedsystems and methods of biometric analysis matching, reference is made tothe accompanying figures, wherein:

FIG. 1 is a block diagram of an exemplary system of enhancing biometricanalysis matching accuracy in accordance with the present disclosure.

FIGS. 2A-H are right and left iris pairs of four subjects showingsimilarities in broadband or coarse features between the right and leftirises.

FIGS. 3A-R are randomly mixed right and left iris pairs of eighteensubjects showing similarities in broadband or coarse features betweenthe right and left irises.

FIG. 4 is a red, green and blue (RGB) iris image showing elongated holesin the iris surface referred to as crypts.

FIG. 5 is an RGB iris image showing no obvious crypts.

FIG. 6 is an image of an NIR illuminated iris showing elongated holes(crypts) in the surface near the pupil.

FIG. 7 is an image of an NIR illuminated iris showing no evidence of thecrypts.

FIG. 8A is an image of an original iris showing 12 of 360 radial spokesalong which iris texture was sampled, FIG. 8B is an image of an irisshowing dashed eyelid masking lines, and FIG. 8C is a 101×361 pixelpolar representation showing the masked region.

FIGS. 9A-H are images of 90×90 pixel “chunks” of iris texture extractedfrom polar iris representations showing the presence of crypts (FIGS.9A-D) and the absence of crypts (FIGS. 9E-H).

FIGS. 10A-F are randomly chosen CASIA-Lamp irises in polarrepresentation showing crypts, particularly near the pupil boundary.

FIGS. 11A-C are NIR illuminated iris images showing Wolfflin nodulesindicated by dotted white lines at a roughly constant radius about theiris center.

FIGS. 12A-B are representations from AlexNet's deep dreams of iriseswith crypts (FIG. 12A) and without crypts (FIG. 12B) representing inputto the network that results in strong activation of either the crypt orno crypt output neurons.

FIG. 13 is a flowchart illustrating an exemplary process of implementinga system of enhancing biometric analysis matching accuracy in accordancewith the present disclosure.

FIG. 14 is a block diagram of an exemplary system of enhancing biometricanalysis matching efficiency in accordance with the present disclosure.

FIG. 15 is a diagrammatic representation of an N-membered enrollmentdatabase reordered to provide top priority to irises most closelymatching broadband features of a probe iris.

FIG. 16 is a diagrammatic representation of an N-membered enrollmentdatabase reordered to provide top priority to irises most closelymatching broadband features of a probe iris.

FIG. 17 is a block diagram of an exemplary computing device forimplementing the exemplary systems of biometric analysis in accordancewith the present disclosure.

FIG. 18 is a block diagram of an exemplary biometric analysis systemenvironment in accordance with the present disclosure.

DESCRIPTION OF EXEMPLARY EMBODIMENTS

In accordance with embodiments of the present disclosure, exemplarysystems of enhancing biometric analysis matching are provided that use acombination of short-range and broadband iris texture (e.g., dualmodalities) to increase the confidence in the determination of whether amatch between a presented and an enrolled iris is found. Increasing theconfidence in the match determination allows the system to efficientlyauthenticate a subject without additional probe image capture. Forexample, if a low-confidence match is found based on the short-rangeiris features, rather than capturing an additional probe image to ensurethe match, the system can rely on the broadband iris features capturedin the same image to confirm a positive match. As used herein, alow-confidence match can refer to a “gray zone” for a match somewhere inthe range between a high-confidence match and a high-confidencenon-match, or a match having a match score better than (e.g., above) athreshold value.

The exemplary system analyzes iris texture for the purpose of irisrecognition in which iris characteristics identified by deep learningnetworks (e.g., neural networks, computational vision algorithms, or thelike) are used as a second modality in conjunction with short-rangescale texture that is generally used. In some embodiments, the exemplarysystems use the broadband iris texture as a second modality to reinforcethe biometric analysis based on the short-range iris texture if theanalysis based on the short-range iris texture is at a low-confidencelevel. In some embodiments, the biometric analysis of the short-rangeiris texture can be performed prior to the biometric analysis of thebroadband iris texture. In some embodiments, the biometric analysis ofthe short-range and broadband iris texture can be performedsubstantially simultaneously.

In general, iris texture analysis for iris recognition follows theseminal work of Daugman, which demonstrates that Gabor wavelets ofproperly chosen length scale can encode the texture of an iris that isunique among virtually all other irises. See, e.g., J. Daugman, “HighConfidence Visual Recognition of Persons by A Test of StatisticalIndependence”, IEEE Trans. Pattern Anal. Machine Intelligence, vol. 15,No. 11, pp. 1148-1161 (1993). The length scale of Daugman's Gaborwavelet-based system is short, thereby avoiding problems associated withshading due to illumination and other longer length scale phenomena.Problems can occur, however, in cases of low-confidence matches, e.g.,matches between a probe iris introduced to a system and enrolled irisesin the system database that match with a score close to the worstacceptable matching threshold. In such instances, the biometric analysissystem determines whether to accept such low-confidence match as a truepositive and, if wrong, cause a false positive match, or reject thematch as a false negative and, if wrong, cause a false negative match.

The exemplary system introduces new evidence in the form of a secondmodality that can be brought to bear on the question of whether a matchexists in low-confidence matching cases. It should be understood thatthe exemplary system can use the second modality in non-low-confidencematching cases to reinforce the match determination by the system. Thesecond modality of the exemplary system can be in the form of coarse orbroadband iris texture that is determined using machine learning,machine vision and/or other signal processing or computer visiontechniques. In the case of a low-confidence match, the broadband iristexture match determination can be used to weigh the match determinationof the system. The fusion of both short and broadband iris textureanalysis improves the overall accuracy of the biometric authenticationprocess.

For example, the broadband iris texture can add weight to thedetermination of “no match” if the broadband iris texture is determinedto be different in the probe and enrolled iris pair in question. As afurther example, the broadband iris texture can add weight to thedetermination of a “match” if the broadband texture of the probe andenrolled iris are determined to be sufficiently similar. The additionalanalysis and determination gleaned from the coarse or broadband iristexture is uncorrelated to the information used by traditional irisbiometric matching systems and, as such, provides the advantage ofreducing matching errors by adding information to the decision process.Particularly, rather than relying on only short-range iris texture thatmay be conducive to false positives and false negatives, the broadbandiris texture provides a secondary means of reinforcing the matchdetermination performed by the biometric analysis system. The broadbandiris texture therefore adds information to the evaluation of a biometricmatch, acting as an additional biometric modality. In some embodiments,both the short-range and broadband iris texture can be captured by thesystem using the same input information, allowing for the exemplarysystem to operate efficiently and effectively. It should be understoodthat the weight given to the short and broadband features during theanalysis based on respective match or non-match results can vary basedon a fusion algorithm tuned to the input imagery. For example, Table 1below provides various algorithmic decisions that can be performed bythe exemplary system.

TABLE 1 Algorithmic Decisions By System Based On Match StatusShort-Range Broadband Result Example 1 Low-Confidence Non-MatchNon-Match Match Example 2 Low-Confidence High-Confidence Match MatchMatch Example 3 High-Confidence Low-Confidence Match Match Match Example4 High-Confidence High-Confidence Match Match Match

With reference to FIG. 1, a block diagram of an exemplary system 100 ofenhancing biometric analysis matching (hereinafter “system 100”) isprovided. As will be discussed in greater detail below, the system 100is configured to collect at least one probe image having bothshort-range and broadband iris textural information that is used todetermine whether a biometric authentic match exists between the probeimage(s) and enrollment image(s). Rather than relying solely on theshort-range iris textural information for the authentication matchdetermination, the system 100 relies on the broadband iris texturalinformation as a supplemental authentication match determination thateither reinforces the authentication match determination inhigh-confidence cases or acts as a final deciding factor inlow-confidence cases. By implementing the broadband iris texturalinformation as a supplemental factor in the authenticationdetermination, the system 100 ensures higher confidence in true positiveand true negative determinations, and a reduction in false positive andfalse negative determinations. In some embodiments, rather than beingused in combination with the short-range iris textural information, thebroadband iris textural information can be used in combination with anyother biometric modality, e.g., facial structure recognition,fingerprint scanning, gait analysis, infrared palm scanning, voicedetection, combinations thereof, or the like.

The system 100 includes at least one illumination source 102 and atleast one camera 104. The illumination source 102 can be a near infraredlight. The illumination source 102 is configured to illuminate one orboth irises of the subject. In some embodiments, the illumination source102 can be configured to illuminate at least one facial featuresurrounding the irises. In some embodiments, a single illuminationsource 102 can be used to illuminate the iris. In some embodiment,multiple independently controlled illumination sources 102 can be usedto selectively illuminate one or both irises of the subject. Suchillumination can occur simultaneously, synchronously, in a pulsedmanner, or the like.

The camera 104 is configured to capture at least one image of one orboth irises of the subject. The images captured by the camera 104 can bein the form of enrollment images electronically transmitted to andstored in at least one database 106 as enrollment data 108. Theenrollment data 108 can be collected during enrollment of the subjectinto the system 100. The images of the enrollment data 108 includeinformation or data corresponding to both short-range iris texture 110(e.g., fine iris texture) and broadband iris texture 112 (e.g., coarseiris texture) to be used during the authentication process. It should beunderstood that the enrollment data 108 can be collected over a periodof time by at least one camera 104 such that the authentication processof the system 100 considers images captured during different lighting orenvironmental conditions, as well as biometric changes associated withthe subject over time.

The images captured by the camera 104 can be in the form of probe images114 electronically transmitted to and stored in the database 106. Theprobe images 114 include information or data corresponding to bothshort-range iris texture 116 and broadband iris texture 118. During theauthentication process, capture of the at least one probe image 114results in capture of iris biometric data (both short and broadband)associated with the iris of the subject. In some embodiments, capture ofthe short and broadband iris texture 116, 118 can be accomplished in thesame step or image, such that additional operation of the system 100 isnot needed. For example, the data found in capturing a probe image 114that includes short-range iris features can also be used to extract thedata needed for constructing a broadband iris feature vector. In someembodiments, capture of the short and broadband iris texture 116, 118can be accomplished in two or more consecutive steps.

The system 100 can include a communication interface 120 configured toprovide for a communication network between components of the system100, thereby allowing data to be electronically transmitted and/orreceived by the components of the system 100. The system 100 can includeat least one processing device 122 with a processor 124 for receivingand processing the data captured by the camera 104. In some embodiments,the processing device 122 can receive the data captured by the camera104 and electronically transmits such captured data to a centralcomputing system 126 for analysis and processing. The processing device122 can be in communication with and programmed to control operation ofthe camera 104 and illumination source 102. The processing device 122receives as input camera imagery (e.g., probe images 114), analyzes thecamera imagery, and contributes to the determination of whether apositive or negative match occurs.

The processing device 122 can receive as input the probe image 114including both the short-range iris texture 116 and broadband iristexture 118. In some embodiments, the short and broadband iris texture116, 118 can be input as a single image 114. In some embodiments, theshort and broadband iris texture 116, 118 can be input as separate,individual images 114. The processing device 122 can analyze theshort-range iris texture 116 of the probe image 114 relative to theshort-range iris texture 110 of the enrollment data 108 to determineiris biometric authenticity for the short-range.

The processing device 122 can further analyze the broadband iris texture118 of the probe image 114 relative to the broadband iris texture 112 ofthe enrollment data 108 to determine iris biometric authenticity for thebroadband. Based on the biometric authenticity of the short andbroadband iris texture 116, 118, the processing device 122 is capable ofdetermining the biometric authenticity of the subject. For example, theprocessing device 122 can analyze the short and broadband iris texture116, 118 in the probe image 114 relative to the short and broadband iristexture 110, 112 of the enrollment data 108 to determine whether abiometric match is found. The broadband iris texture 112, 118 analysisacts as a secondary modality to reinforce the findings of theshort-range iris texture 110, 116 analysis. For example, if theshort-range iris texture 110, 116 analysis results in a low-confidencepositive match, a positive match of the broadband iris texture 112, 118analysis can reinforce the short-range positive match. As a furtherexample, if the short-range iris texture 110, 116 analysis results in along-confidence positive match, a negative match of the broadband iristexture 112, 118 analysis can output an overall negative match. In someembodiments, the authentication analysis based on the broadband iristexture 118 can be used as a final deciding factor in confirming whetherthe authentication analysis based on the short-range iris texture 116resulted in the correct determination. In some embodiments,authentication matching can be performed between the probe image 114 andan N-membered enrollment database (e.g., the enrollment data 108). Insome embodiments, authentication matching can be performed between theprobe image 114 and a single enrolled iris in the enrollment data 108.

The system 100 includes a user interface 130. In some embodiments, theuser interface 130 can include a display in the form of a graphical userinterface (GUI) 132. In some embodiments, the interface 130 can includea numerical (or alphanumerical display), the illumination source 102,the camera 104, combinations thereof, or the like. For example,instructions for properly using the system 100 can be provided to theuser via the GUI 132. The GUI 132 can include at least one display orindicator for communicating information to the subject, such as theresults of the authentication process.

Although discussed herein as authentication based on a single iris, itshould be understood that in some embodiments, the system 100 canperform the authentication analysis on both irises of the subject. Forexample, rather than authenticating the subject based on biometricmatching of a single iris (e.g., a left iris), both left and rightirises of the subject can be analyzed relative to the corresponding leftand right iris enrollment data. Data associated with results ofauthentication can be electronically stored in the database 106 asauthentication data 128. In some embodiments, the authentication data128 can be used to improve operation of the system 100 through, e.g.,machine learning, machine vision, or the like. For example, afteriterations of the authentication process, the authentication data 128can be analyzed for false positives and false negatives to determinewhich features of the system (if any) can be varied to reduce the chanceof false positives and false negatives.

As an example, the system 100 can initially perform the authenticationanalysis based on the short-range iris texture 116, 110 of the probeimage 114 and enrollment data 108 to determine if a biometricauthenticity match is found based on the short-range iris features. Insome cases, short-range iris texture authentication on its own can bedifficult and error prone. The system 100 can generate a scorecorresponding with the degree to which a biometric match was found basedon the short-range iris features. If the result of the short-rangeauthentication process produces a low-confidence match, e.g., a matchwith a score close to the worst acceptable matching threshold, thesystem 100 can perform the authentication analysis based on thebroadband iris texture 118, 112 of the probe image 114 and enrollmentdata 108 to determine if a biometric authenticity match is found basedon the broadband iris features. In some embodiments, the short andbroadband authentication analysis can be performed substantially at thesame time.

If the result of the broadband authentication process produces ahigh-confidence positive match and the short-range authenticationprocess produced a low-confidence positive match, the broadbandauthentication result can be used to confirm that a positive biometricmatch occurred. If the result of the broadband authentication processand the result of the short-range authentication process both produces alow confidence positive match, the broadband authentication result canbe used to confirm that a negative biometric match occurred.

Similarly, if the result of the broadband authentication processproduces a high-confidence negative match and the short-rangeauthentication process produced a low-confidence negative match, thebroadband authentication result can be used to confirm that a negativebiometric match occurred. If the result of the broadband authenticationprocess and the result of the short-range authentication process bothproduces a low confidence negative match, the broadband authenticationresult can be used to confirm that a negative biometric match occurred.In instances of a negative biometric match confirmation, the system 100can request an additional probe image 114 from the subject.

Broadband features in the texture of an iris of the probe image 114 canfocus on general features of the iris, as compared to features justfound only in the short-range analysis, and can be determined using anynumber of schemes including but not limited to signal processing,computer vision methods, deep learning, machine learning, machinevision, or the like. In the case of machine learning, the system 100 cantrain on a variety of irises to capture and produce broadband irisfeatures, and such broadband iris features learned by a network can beused to characterize an iris. In the case of signal processing orcomputer vision, the system 100 can use an algorithm for computationalvision methods with a filter to detect and select radial transitionsbetween a region of an iris with a broadband feature (e.g., acollarette) and a region beyond the broadband feature.

In some embodiments, the broadband feature analysis can determine thedegree to which the features in the probe image 114 are present, and thecoarsely defined region where the feature is found, e.g., in one of twoannular rings, inner half of the iris, or outer half of the iris. Forexample, the system 100 can determine the presence of horizontal orvertical stripes in the inner half of the iris closest to the pupil. Thenumber of broadband iris features found in the texture of a probe iriscan be combined (by the processing device 122) into a feature vector. Insome embodiments, the feature vector can include a listing of featuresand their measured strength. It should be understood that the featurevector can be representative of one or more broadband features of aniris, with each component of the feature vector being associated with arespective broadband feature and the magnitude of the component valuerepresenting the strength or intensity of the broadband feature.

During the authentication analysis for the broadband iris texture, themeasured feature vector of the probe iris of the probe image 114 can beanalyzed and compared to the feature vector of an enrolled iris of theenrollment data 108 that is suspected of matching the probe iris, butwith a match score indicative of a low confidence match. Particularly,the system 100 can initially analyze and compare the short-range irisfeatures of the probe image 114 and the enrollment data 108. Suchshort-range iris feature analysis can determine the enrollment imagewith the closest match to the probe iris, and the broadband featurevector can be compared to the same enrollment image to supplement theshort-range feature determination.

As an example, N can represent the number of coarse or broadbandfeatures found in the iris (F₁, F₂, F_(N)), such as crypts, sunbursts,flames, or the like. A feature vector can include N features listing theamount or proportion of each of the N features found to be present inthe iris texture in question. In some embodiments, the amount orproportion of each broadband feature can be represented as a numericalvalue between 0 and 1, with 0 indicating a complete absence of thebroadband feature and 1 indicating the highest level of detection orpresence of the broadband feature. In some embodiments, differentnumerical value ranges can be used to represent the magnitude ofpresence of each broadband iris feature. For example, if N=3 for threebroadband features being analyzed, the feature vector of a first iriscan be represented as (0, 1, 0). Such feature vector can indicate thatfeature F₂ is present while features F₁ and F₃ are absent. A second iriswith a feature vector of (0.1, 0.8, 0.1) may be considered a match tothe first iris vector of (0, 1, 0) due to the smaller F₁ and F₃ valuesand close relationship of the F₂ values. Particularly, a match can befound based on the similarity in the texture of the probe and enrollmentirises.

However, a third iris with a feature vector of (0.4, 0, 0.6) may beconsidered a non-match to the first iris vector of (0, 1, 0) due to thedifference in values for the F₁ and F₃ features and the non-existent F₂feature. In some embodiments, the determination of match or non-matchcan be based on whether the magnitude of the feature is within apredetermined threshold range (e.g., within ±0.2). In some embodiments,comparison of two feature vectors to determine the degree to whichbroadband characteristics indicate a match can use a variety of methodsincluding but not limited to binary comparison of at least one feature,Euclidean distance, non-Euclidean distance, a dot product analysis, aweighted dot product analysis, weighted distance measurement,mathematical comparison of two feature vectors relative to a threshold,or the like.

In some embodiments, the broadband iris features discussed herein caninclude the measurement of the presence of crypts in an iris as aparticular broadband feature. In some embodiments, measurement of thepresence of crypts can be performed without considering the exactposition, size or relative orientation of the crypts. For example,rather than analyzing the probe and enrollment images for a specificquadrant or radial position of a broadband feature, the system cananalyze the images to determine if the broadband feature is detected inany location of the iris. By analyzing the images without consideringthe exact position, size or relative orientation of the broadbandfeature, the system is capable of detecting potential matches even ifthe orientation of the captured iris is different from the orientationof the enrollment images. If measurement of a second iris shows nocrypts or a low level of crypts in position F₁ of the feature vector,such determination can indicate a non-match that outweighs thelow-confidence authentication result based on the short-range irisfeatures. In some embodiments, the system 100 can consider the exactposition, size and/or relative orientation of the crypts. For example,the system 100 can determine that the crypts are present only in theinner portion of the iris nearest the pupil in the enrollment image,while in a second iris (e.g., the probe image 114), similar crypts arefound only in the other portion of the iris near the sclera. Based onsuch determination, the system 100 can indicate that the two irises donot match.

In some embodiments, the focus of a broadband iris texture featureanalysis can be on detecting the existence of—or nature and/or qualityof—the features, rather than focusing simply on exact position and/orshape of a broadband iris feature. However, some embodiments of suchanalysis can so focus on position and/or shape, depending on the natureof the feature vector. Broadband iris features can form either a ring offeatures surrounding the pupil (e.g., crypts) or features that looselyencircle the iris (e.g., nodules). As an example, if the probe image ofthe iris includes a ring of crypts, the system 100 can be configured toanalyze the enrollment image to determine the existence of a ring ofcrypts in the enrollment image. If the system 100 determines that a ringof crypts exists in the enrollment image, whether of the same ordifferent diameter and/or configuration as the crypts in the probeimage, the system 100 can output at least a low-confidence match.

Example Feature Vector

By way of non-limiting example, an example feature vector (F₁, F₂, F₃,F₄, . . . ) is herein described whereby the feature vector is formed ofset member(s) comprising background texture as feature 1 (F₁), collaretas feature 2 (F₂), crypts (F₃), and Wolfflin nodules (F₄), with thefollowing being assigned to the following:

(F₁) Background Texture:

-   -   0=Blank;    -   1=Non-descript;    -   2=Thin Radial Lines (thin being low-radian line width); and    -   3=Fat Radial Lines (fat being high-radian line width.

(F₂) Collaret:

-   -   0=None;    -   1=Barely Discernable;    -   2=Visible; and    -   3=Pronounced.

(F₃) Crypts (such as openings, holes):

-   -   0=None;    -   1=within<¼r;    -   2=within<½r; and    -   3=≥½r;

where r is the distance from the pupil-sclera boundary to the outerperimeter of the scilera (near the skin), and where a lower number iscloser to said boundary and where a higher number is closer to saidouter perimeter, such that F is a measure of the amount of territorythat is occupied with crypts (e.g., 1 would be just a thin inner ring ofcrypts, where 3 would be a fatter ring of crypts that occupy more areaof the sclera extending out from the pupil).

(F₄) Wolfflin Nodules (such as dotted rings of pigmentation in thesclera circling the pupil or other patterns):

-   -   0=None;    -   1=dotted pigmentation forming non-ring-like pattern;    -   2=dotted pigmentation forming a partial ring pattern;    -   3=dotted pigmentation forming a ring pattern; and    -   4=dotted pigmentation forming a double-ring pattern.

In the present feature vector example, F₄ (Wolfflin Nodules) has beenassigned a scale of zero to four, whereas the example assigned scale foreach of F₁, F₂, and F₃ of zero to three. This is to show that theassigned scale for each feature of the feature vector need not includethe same number of selections.

With respect to the above example feature vector and/or otherwisespeaking, a good matching score can be indicative of a match of highconfidence, a low matching score can be indicative of a non-match, and agray region can be indicative of an instance in which a match mayindicate a true positive but with lower confidence than is desired.Although the broadband feature analysis of the system 100 can be used inany of the good, low and gray instances, such operation can beparticularly valuable in cases of matches in the gray region.

If the system 100 determines that the feature vector of the probe image114 and the feature vector of the enrolled iris meet or exceed apredetermined level, referred to herein as a threshold, the broadbandiris feature determination can add confidence and indicates a truepositive match output. If the broadband iris features of the probe image114 and of the enrolled iris do not meet the predetermined threshold,the low-confidence match can be demoted to a non-match output. Thebroadband iris feature analysis therefore provides a second biometricmodality to add confidence to a biometric match based on short-rangeiris features. For example, the broadband iris texture analysis can actas a supplemental factor for resolving ambiguity in the case of alow-confidence (or any confidence level) matching score.

The iris matching algorithms of the system 100 can therefore be used tominimize false positive and false negative errors. Such errors can beminimized in biometric authentication systems that rely on short-rangeiris feature analysis, or can be used in combination with any otherbiometric authentication features. For example, the broadband irisfeature analysis can be used to disambiguate low-confidence matches intraditional biometric analysis systems.

In some embodiments, the system 100 can be used to authenticate thesubject based on analysis of a single iris. In some embodiments, thesystem 100 can be used to authenticate the subject based on analysis ofboth the left and right irises. In such embodiments, at least one camera104 can produce probe images 114 of both the left and right irises ofthe subject, and the system 100 analyzes the probe images 114 relativeto respective left and right iris enrollment images of the subject. Insome embodiments, the analysis of the probe images 114 can be performedin a pair (e.g., both the left and right irises can be grouped togetherand simultaneously matched to respective left and right enrollmentirises), or independently (e.g., the left iris probe is matched to leftiris enrollment images independently from the right iris probe beingmatched to right iris enrollment images). If two low-confidence matchesare found, the system 100 can fuse these results to increase the levelof confidence of a match. For example, if both the left and rightenrolled irises that match with low confidence come from the sameenrolled subject, the confidence of the match can be enhanced. Thebroadband iris feature analysis of the system 100 can further be used toensure that the coarse feature spectrum of the matching iris alsomatches the other iris, e.g., that the left and right irises share athreshold level of coarse features.

In some embodiments, the broadband iris feature analysis of the system100 can be used as a stand-alone iris biometric modality in cases ofdegraded iris images (e.g., out of focus images, images having a lowspatial resolution due to low sampling density, or the like). In suchembodiments, the broadband texture of the iris can be used as a softbiometric in combination with, e.g., face biometrics, fingerprintanalysis, voice recognition, gait analysis, or the like, to produce areliable biometric indicator.

In some embodiments, the system 100 can be used to curate iris databasesin search of clerical errors. Such errors can limit the ultimateaccuracy of any biometric matching system. Because there is generally acorrelation of the broadband feature vectors between a given subject'sleft and right iris, testing a database in search of cases that showvastly different left and right iris feature vectors can signalpotential database errors. The broadband feature vectors can thereforealso be used to ensure accurate iris database maintenance, and canoperate alongside de-duplication in database checking operations.

The irises of identical twins generally do not match. TraditionalDaugman-type iris matching methods show that on a fine scale, the irisesof identical twins are as similar as irises of unrelated subjects. TheDaugman-type methods also register no match between the left and rightiris of the same person which, genetically speaking, are even closer toone another. However, as shown in FIGS. 2A-H, the broadband or coarsesimilarities of left and right irises of given subjects areunmistakable. Particularly, the left and right iris pairs of FIGS. 2A-B,FIGS. 2C-D, FIGS. 2E-F, and FIGS. 2G-H show distinct similarities in thebroadband features. Thus, even if the left and right iris pairs havedifferent short-range features, the left and right iris pairs of thesame subject share substantially similar broadband features. See, e.g.,K. Hollingsworth et al., “Similarity of iris texture between identicaltwins,” 2010 IEEE Computer Society Conference on Computer Vision andPattern Recognition—Workshops, 13-18 June 2010, pp. 22-29 (2010).

Experimentation was performed to determine the effectiveness ofbroadband or coarse iris textures for iris matching. A collection ofleft and right NIR irises images of sixteen individuals, chosen atrandom, was constructed from a larger database. A preliminary experimentused irises of half of the selected subjects. In the preliminaryexperiment, the sixteen irises of eight subjects were laser printed oncopy paper. The irises were cut from the larger eye image to eliminatethe sclera and any evidence of eyelids or eyelashes. The irises werethen attached to a single sheet of paper in a random order with randomrotational orientation and each iris was labeled with a letter from A toP. Participants in the experiment were instructed to seek and record theletter identifiers of left-right iris pairs. Each of the participantssucceeded in matching all of the left-right pairs correctly using thecoarse iris texture as the primary cues for matching. It was noted thatthe iris gray-shade, position of specular reflections, shape of theirises after eyelid removal, and overall iris size provided secondarycues for matching.

Irises for the subsequent experiment were extracted from the second setof sixteen irises. Two random irises, ringers from each of two subjectsof the first set, were added to create a set of eighteen irises. Thegray-shade of each iris was shifted slightly and at random. Instead ofusing the precise iris-sclera boundary along which to cut, anear-circular boundary was produced that avoided eyelids and eyelashesand gave no cues about eye orientation. Isolated irises were rotated andslightly resized at random and arranged at random on a single page withspecular reflections painted black. The left and right irises for theexperiment are shown in FIGS. 3A-R. The correct pairings are indicatedby the matching numbers within each iris, e.g., 1 with 1, 2 with 2,etc., with “x” representing the two random irises. The irises weredistributed electronically to eliminate the role of printer artifacts.Participants of the experiment were able to identify four of the eightpairs of subjects (1, 2, 3 and 6) correctly and often confused one ofthe random irises (FIG. 3E) with the correct pairing of subject 4 (FIGS.3D-F). The remaining three pairs (5, 7 and 8) proved more difficult andwere identified correctly by about half of the participants. The resultsof the experiments indicated that the apparent similarities between theNIR images of left and right irises of a given subject derive frombroadband, human-readable iris texture that is on a scale outside of theDaugman algorithm filter window, but well within a human usable scale.The experiment also emphasized that the broadband iris texture can beused to reinforce traditional biometric matching operation. Theexperiment further emphasized that the broadband iris texture can beused to perform an initial biometric analysis for optimizing reorderingof the enrollment data in a biometric analysis system (e.g., system 200discussed below) prior to authentication performed based on theshort-range iris texture.

Additional experiments were performed in training a deep learningnetwork for matching irises based on broadband iris features. TheAlexNet network through MATLAB's neural network toolbox was implemented.See, e.g., Krizhevsky, A. et al., “ImageNet Classification with DeepConvolutional Neural Networks”, NIPS 2012: Neural Information ProcessingSystems, Lake Tahoe, Nev. (2012). The AlexNet network includes a seriesof five convolutional layers (plus a combination of ReLu, normalizationand pooling layers) and two fully connected layers, ending with asoftmax layer for classification. The network had previously beentrained using a large number of 227×227 RGB images from a large numberof classes. The network was retrained by changing two of the lastlayers. The last fully connected layer included only two neurons and thefinal layer classified the presence or absence of a particular irisfeature known to ophthalmologists. It should be understood that thenumber of features used can be expanded to more comprehensivelycharacterize features of irises. The particular feature fixated on forexperimentation is referred to as a crypt (e.g., a Fuchs' Crypt).Presence of crypts has been shown to correlate to genetic markers thatin turn correlate to particular demographic populations. See, e.g., M.Edwards et al., “Analysis of iris surface features in populations ofdiverse ancestry,” Royal Soc. Open Sci., vol. 3:1 (January, 2016)(published on-line). FIG. 4 shows an RGB image of an iris with suchcrypts visible as elongated holes in the iris surface, while FIG. 5shows an RGB iris image without obvious crypts.

In the NIR spectrum with direct frontal illumination that creates lessshadowing, crypts (e.g., Fuchs' Crypts) of different eyes can be seen inFIG. 6 and not in FIG. 7. Contrast in the NIR images of FIGS. 6 and 7 isnot as dramatic as illustrated in the visible light illuminated imagesof FIGS. 4 and 5. As an initial experiment to characterize irises usingmachine learning, irises were categorized as either having crypts ornot. The following procedure was used to produce the “Crypts” and“NoCrypts” datasets.

During a first conversion step shown in FIGS. 8A-B, the original 640×4808-bit iris images were converted to Kind 7 masked irises. See, e.g., P.Grother et al., “IREX I, Performance of Iris Recognition Algorithms onStandard Images,” NIST Interagency Report 7629, p. 2 (September 2009).During a second conversion step shown in FIG. 8C, the Kind 7 maskedirises were sampled on 101 evenly spaced points along each of the 360radial spokes from the pupil-iris boundary to the iris-sclera boundaryincluding the masked eyelid area. The polar sampling was plotted as anormalized rectangle similar to that used in iris recognitionalgorithms.

During a third conversion step shown in FIGS. 9A-H, 90×90 pixel chunksof the iris were sampled from polar iris representations of similar toFIG. 8C, aiming to cover as much of the iris texture as possible whilelimiting the masked area to less than 5% of the total area of any chunk.FIGS. 9A-D show the presence of crypts, while FIGS. 9E-H show an absenceof crypts. Dark or black regions of FIGS. 9C, D and G represent eyelidmasks, while light or white regions of FIG. 9H represent Wolflinnodules, e.g., textural features that can be learned but are were notrelied on in the experimentation. The individual chunks of FIGS. 9A-Hwere labeled on the basis of whether crypts were present or not,resulting in two labeled populations.

Irises for the experiment came from forty-nine-subjects in the form of640×480 8-bit NIR iris images of the standard type. Three transactionsfor each subject provided a total of 290 irises. Of these, 262 segmentedsuccessfully and were transformed to polar representations. From thepolar representations, 616 90×90 pixel chunks as shown in FIGS. 9A-Hwere generated, 251 of which were labeled as including crypts and theremaining 365 were labeled as no crypts. These two groups of iristexture patches formed the input to the machine learning exercise.

Particularly, the two groups of iris texture patches formed the inputfor the AlexNet deep learning network and the network was modified tolearn two classes of iris texture patches (with and without crypts).Training data representing 80% of the total of each class was chosen atrandom from the two groups of iris texture patches. Training usedMATLAB's forward and reverse propagating limited memoryBroyden-Fletcher-Goldfarb-Shanno (LBFGS) algorithm to minimize loss infidelity as the images were propagated back and forth through themultiple layers of neurons.

After twenty passes through the full data set (20 epochs), the loss infidelity stabilized at an asymptotic limit while the training dataaccuracy peaked at close to 100%. Training data accuracy is not a truemeasure of the predictive ability of a trained network. Generally,training data based on a learning group is used to train a neuralnetwork, and the effectiveness of the trainer network is tested on adataset from a testing group previously unseen by the neural network.Testing with the remaining 20% of the data (123 iris texture patches, 50Crypts, and the rest NoCrypts) showed between 90 and 92% accuracy withrepeated trials using randomly chosen test data. Interpreted in terms oferror rate, the result shows that of every 100 iris texture patchesexamined by the deep learning network, 8 to 10 errors are expected inwhich either an iris texture patch with crypts is predicted to have noneor an iris texture patch without crypts is predicted to have some. Whenexamined, the error cases listed in Table 2 for one sample runinvariably included ambiguous textures that proved difficult to manuallyclassify. Thus, with supervised learning, the modified deep learningnetwork AlexNet can successfully distinguish one particular texturalfeature of irises with about 90% accuracy.

TABLE 2 Confusion Matrix For Text of Modified AlexNet On Iris TexturePatches With and Without Crypts Predicted to Have Predicted to HaveCrypts No Crypts Manually selected 45 5 to have crypts Manually selected5 68 to have no crypts

The CASIA-Lamp (Chinese Academy of Sciences Institution of AutomationLamp) database includes twenty left and twenty right irises of 411Chinese subjects, mostly university students. FIGS. 10A-F are randomlychosen CASIA-Lamp irises in polar representation showing crypts,particularly near the pupil boundary. Such crypts can be detected by theexemplary system during broadband iris feature analysis.

Another iris texture feature recognized by ophthalmologists and referredto as Wolflin nodules appear in NIR images as white blobs often locatedat constant radius positions forming circular arcs of dots relative tothe iris center. FIGS. 11A-C are examples of NIR images showing iriseswith Wolfflin nodules. The exemplary system and network can be trainedto identify Wolfflin nodules as one of the broadband iris features foridentification.

The system can be trained to identify additional iris textural features,such as collarettes, radial spokes, looping (spirograph-like) light ordark lines, combinations thereof, or the like. Some of the features canbe detected by searching for azimuthal angle-independent patterns. Someof the features can be detected by searching for particular spatialfrequencies in the azimuthal direction or by more complex signalanalysis on the irises in the polar representations.

The system can also be trained to detect human non-readable features.Training a network on an ensemble of irises can automatically generatefeatures. A deep learning network can develop its own features onincreasing length scales as small filters in convolutional layers thatfind edges combined to find corners and further combined to formpatterns of increasing complexity. The complex features of a deeplearning network can be referred to as deep dreams in reference to theoutput of a reverse propagating signal that starts as input into asingle output (classification) neuron. Textbook examples using networkstrained to distinguish dogs, cats, frogs, trucks, cars, or the like, cangenerate deep dreams of any of the trained classes giving insight intowhat a network thinks when asked to find a particular object. In thecase of the modified AlexNet that distinguishes irises with crypts andno crypts, deep dreams reveal stark differences between the categoriesas shown in FIGS. 12A-B. Distinct foci were observed in the deep dreamof irises with crypts, but only broad textures were observed in deepdreams of irises without crypts. Each dream can represent a stimulustuned to produce high activation in the neuron classifying acorresponding object.

FIG. 13 is a flowchart illustrating an exemplary process 200 ofimplementing the system 100. To begin, at step 202, the iris of thesubject is illuminated with at least one illumination source. At step204, at least one probe image of the iris of the subject can be capturedwith at least one camera. Each of the probe images has iris biometricdata in the form of short and broadband iris texture information. Atstep 206, a processing device can receive as input the at least oneprobe image. At steps 208 and 210, the short and broadband iris textureinformation can be analyzed by the processing device for iris biometricauthenticity. At step 212, based on the biometric authenticity of theshort and broadband iris texture information, the processing device candetermine the biometric authenticity of the subject.

In accordance with another embodiment of the present disclosure,exemplary systems of enhancing biometric analysis matching efficiencyare provided that use a combination of short-range and broadband iristexture analysis to efficiently and accurately perform theauthentication analysis. Traditionally, the time spent matching a probeiris template against an enrollment database increases with databasesize and can become burdensome with large databases. Brute forceaddition of parallel computing resources and more cores may reduceoverall matching times, but generally involves an increase in costs forboth the additional equipment and maintenance. However, in the presentdisclosure, by implementing both short-range and broadband iris textureanalysis, exemplary systems disclosed herein provide an efficientanalysis and search of an enrollment database during the authenticationprocess, thereby reducing matching time per core and relieving the needfor extra hardware, thereby preferably enhancing biometric matchingefficiency. Particularly, rather than focusing on a random search formatches within the enrollment database, the exemplary systems reorderthe enrollment database based on the broadband iris feature relevance ofthe enrolled irises. Such reordering allows for the biometric matchingbased on the short-range iris features to be performed starting with themost relevant enrolled irises, ensuring that a positive match can befound in a faster time period.

As noted above, traditional biometric analysis systems generallydisregard broadband iris texture and instead focus on short-range iristexture evaluation. Biometric analysis based on the short-range irisfeatures can be time-consuming and error prone due to the excessive sizeof the database storing enrollment images and due to the random natureof the matching process. For example, finding a match between a probeimage and an enrollment image stored in a database having over onemillion enrollment images can result in a lengthy period of time. As afurther example, if a digital iris code for the short-range irisfeatures starts with the bits 10010001, there is no guarantee that anauthentic match would share the same starting bits in its code. Becauseof the vagaries of iris encoding, the bits representing an iris aregenerally uncertain.

The exemplary system incorporates the broadband or coarse iris textureanalysis as a means for providing a more efficient authenticationprocess. As large biometric iris databases find increasing use innational identification programs, travel programs, or the like, theauthentication process will necessitate larger searches. Inpublic-facing applications, it is essential that the authentication andtransaction time be minimized. The exemplary system ensures atime-efficient authentication process for enrollment databases of anysize, reducing undesirable lag time and improving overall operation ofthe biometric analysis system.

During the enrollment process, the system can capture and electronicallyrecord both the short-range and broadband iris texture for a subject.During a subsequent authentication process, at least one probe image canbe captured and the system can extract both the short-range andbroadband iris texture from the probe images. Rather than searching fora match between only the short-range iris texture of the enrollment andprobe images, the system implements the broadband iris texture toinitially reorder the database of enrolled irises based on detectedmatches between the broadband iris texture features.

Particularly, the system can be configured to detect the closest matchesof the probe broadband iris texture and the enrollment broadband iristexture, and generates a reordered form of the enrolled irises with theclosest matches closer to the front or top of the database. Instead of arandom or sequential matching between the probe and enrollment images,the exemplary system provides an optimized matching order for each probeiris that is determined by the coarse or broadband iris features in theprobe image. In some embodiments, the coarse or broadband iris texturecan be determined using machine learning, machine vision, and/or othersignal processing or computer vision techniques. The optimized order ofthe enrollment data based on matching of the broadband iris featuresincreases the likelihood that a match occurs early in the list duringthe subsequent short-range authentication analysis.

After the optimized matching order has been generated, the system cananalyze the short-range iris texture of the probe image relative to thereordered enrollment images, starting with the closest broadband iristexture matches. Analyzing the short-range iris texture based on thereordered enrollment images ensures that a match can be found in a moreefficient manner.

With reference to FIG. 14, a block diagram of an exemplary system 300 ofenhancing biometric analysis matching efficiency (hereinafter “system300”) is provided. The exemplary system 300 can identify humannon-readable features that can serve to classify coarse iris texture toaccelerate iris matching using large databases. As will be discussed ingreater detail below, the system 300 is configured to collect at leastone probe image having both short-range and broadband iris texturalinformation, and the broadband iris textural information is used tooptimize the order of the enrollment images prior to analysis of theshort-range iris textural information to determine whether a biometricauthentic match exists between the probe image(s) and enrollmentimage(s).

Rather than relying solely on the short-range iris textural informationfor screening through an enrollment database during the authenticationmatch determination, the system 300 relies on the broadband iristextural information as an initial analysis or screening step tooptimize the order of the enrollment images within the enrollmentdatabase. Particularly, the broadband iris textural information isinitially used to reorder the enrollment database with the closestbroadband iris matches at the top or front of the enrollment database.After the order of the enrollment database has been optimized based onthe broadband iris textural information, the system 300 applies thematch determination based on the short-range iris textural informationstarting with the closest broadband iris textural information matches.By optimizing order of the enrollment images prior to the short-rangeiris textural information analysis, the system 300 ensures anauthentication match can be achieved in a more efficient manner than therandom matching performed by traditional authentication systems.

In some embodiments, rather than being used in combination with theshort-range iris textural information, the broadband iris texturalinformation can be used in combination with any other biometricmodality, e.g., facial structure recognition, fingerprint scanning, gaitanalysis, infrared palm scanning, voice detection, combinations thereof,or the like. The broadband iris features can therefore be used togenerate an optimized order for biometric modalities other than theshort-range iris features. For example, the broadband iris features canbe used to generate an optimized order of an enrollment database havingfingerprint data, and the fingerprint matching analysis can be performedon the optimized order of the enrollment data starting with the enrolledfingerprints that have the closest broadband iris feature match.

The system 300 includes at least one illumination source 302 and atleast one camera 304. The illumination source 302 can be a near infraredlight. The illumination source 302 is configured to illuminate one orboth irises of the subject. In some embodiments, the illumination source302 can be configured to illuminate at least one facial featuresurrounding the irises. In some embodiments, a single illuminationsource 302 can be used to illuminate the iris. In some embodiment,multiple independently controlled illumination sources 302 can be usedto selectively illuminate one or both irises of the subject. Suchillumination can occur simultaneously, synchronously, in a pulsedmanner, or the like.

The camera 304 is configured to capture at least one image of one orboth irises of the subject. The images captured by the camera 304 can bein the form of enrollment images electronically transmitted to andstored in at least one database 306 as enrollment data 308. Theenrollment data 308 can be collected during enrollment of the subjectinto the system 300. The images of the enrollment data 308 includeinformation or data corresponding to both short-range iris texture 310and broadband iris texture 312 to be used during the authenticationprocess. It should be understood that the enrollment data 308 can becollected over a period of time by at least one camera 304 such that theauthentication process of the system 300 considers images capturedduring different lighting or environmental conditions, as well asbiometric changes associated with the subject over time.

The images captured by the camera 304 can be in the form of probe images314 electronically transmitted to and stored in the database 306. Theprobe images 314 include information or data corresponding to bothshort-range iris texture 316 and broadband iris texture 318. During theauthentication process, capture of the at least one probe image 314results in capture of iris biometric data (both short and broadband)associated with the iris of the subject. In some embodiments, capture ofthe short and broadband iris texture 316, 318 can be accomplished in thesame step or image, such that additional operation of the system 300 isnot needed. For example, the data found in capturing a probe image 314that includes short-range iris features can also be used to extract thedata needed for constructing a broadband iris feature vector. In someembodiments, capture of the short and broadband iris texture 316, 318can be accomplished in two or more consecutive steps.

The system 300 can include a communication interface 320 configured toprovide for a communication network between components of the system300, thereby allowing data to be electronically transmitted and/orreceived by the components of the system 300. The system 300 can includeat least one processing device 322 with a processor 324 for receivingand processing the data captured by the camera 304. In some embodiments,the processing device 322 can receive the data captured by the camera304 and electronically transmits such captured data to a centralcomputing system 326 for analysis and processing. The processing device322 can be in communication with and programmed to control operation ofthe camera 304 and illumination source 302. The processing device 322receives as input camera imagery (e.g., probe images 314), analyzes thecamera imagery, provides an optimized order of the enrollment data 308,and contributes to the determination of whether a positive or negativematch occurs. The processing device 322 can be in communication with thedatabase 306.

The processing device 322 can receive as input the probe image 314including both the short-range iris texture 316 and broadband iristexture 318. In some embodiments, the short and broadband iris texture316, 318 can be input as a single image 314. In some embodiments, theshort and broadband iris texture 316, 318 can be input as separate,individual images 314. The processing device 322 can generate anoptimized order 334 of the enrollment data 308 based on execution ofbiometric analysis of the probe broadband iris texture 318 relative tothe enrollment broadband iris texture 312. Traditional biometricauthentication systems generally compare the short-range iris texture316 of the probe image 314 to the short-range iris texture 310 of theenrollment data 308 in a random manner or order, until a potential matchis found or a lack of a match is determined.

The exemplary system 300 seeks to improve the efficiency of theauthentication process by reducing the time for determining whether amatch between the probe image 314 and the enrollment data 308 is found.The processing device 322 performs the biometric analysis of thebroadband iris texture 318, 312 first to locate potential matches basedon the broadband or coarse features of the iris of the subject. Based onthe results of the broadband iris texture 318, 312 analysis, the system300 outputs and electronically stores the optimized order 334 of theenrollment data 308. Such optimized order 334 includes a listing of theenrollment data 308 ordered by closest match to furthest match betweenthe probe and enrollment broadband iris texture 318, 312. For example,if the enrollment data 308 includes one hundred enrollment images forsubjects with both broadband iris texture 312 and short-range iristexture 310 for each subject in a random order, the processing device322 generates a re-ordered and optimized listing of the enrollment data308 with the closest broadband iris texture 318, 312 match at the top ofthe list and decreasing closeness between the broadband iris texture318, 312 leading to the end or bottom of the list. Such optimized order334 ensures that the top of the list is enriched with irises having anincreased chance of matching the probe iris based on the short-rangeiris features.

Once the optimized order 334 has been generated, the processing device322 can analyze the iris biometric data for biometric authenticity basedon the probe and enrollment short-range iris texture 316, 310 startingwith the closest match between the probe and enrollment broadband iristexture 318, 312. Particularly, rather than randomly analyzing theenrollment data 308 until a match is found based on the short-range iristexture 316, 310, the processing device 322 initiates the biometricanalysis with the enrollment data 308 matching the closest based on thebroadband iris texture 318, 312. If a match between the short-range iristexture 316, 310 is not found with the closest broadband iris texture318, 312 enrollment data 308, the processing device 322 moves down theoptimized order 334 sequentially performing the biometric analysis usingthe short-range iris texture 316, 310 until a match is found (or a lackof a match is determined). By focusing first on the enrollment data 308having the closest broadband iris texture 318, 312, a match based on theshort-range iris texture 316, 310 is more likely to be found in ashorter time period than randomly analyzing the enrollment data 308.

In some embodiments, the optimized order 334 can be generated byimplementing feature vectors for each of the probe and enrollmentbroadband iris texture 318, 312. For example, a set of distances betweenthe feature vector of the probe broadband iris texture 318 and theenrollment broadband iris texture 312 can be generated by the processingdevice 322 based on the match of the coarse or broadband features. Insome embodiments, the set of distances between the feature vectors canbe defined by a Euclidian distance determination. The set of distancesbetween the feature vectors can represent or characterize the similaritybetween the feature vector of the probe and enrollment broadband iristexture 318, 312.

For example, a short or small distance between the feature vectors cancorrespond with a close match between the probe and enrollment broadbandiris texture 318, 310. Similarly, a long or large distance between thefeature vectors can correspond with a far (e.g., low) match between theprobe and enrollment broadband iris texture 318, 312. In suchembodiments, the optimized order 334 can include enrollment data 308with the shortest or smallest distance for the broadband iris texture312 at the beginning or top of the list, with decreasing order based onincreasing distance of the feature vectors. The shortest or smallestdistance vectors can therefore be positioned at the top or start of thelist, while the longest or largest distance vectors can be positioned atthe bottom or end of the list. The processing device 322 can perform thebiometric analysis based on the short-range iris texture 316, 310starting with the short-range iris texture 310 corresponding with thebroadband iris texture 312 having the shortest or smallest distancevectors. Based on the biometric analysis of the short-range iris texture310, 316, the system 300 can determine the biometric authenticity of thesubject.

In some embodiments, authentication matching can be performed betweenthe probe image 314 and an N-membered enrollment database (e.g., theenrollment data 308). Such matching is performed by optimizing the orderof the enrollment data 308 by the type of broadband iris texture 312,the intensity of the broadband iris texture 312, the size of thebroadband iris texture 312, or the like. The system 300 includes a userinterface 330. In some embodiments, the user interface 330 can include adisplay in the form of a graphical user interface (GUI) 332. In someembodiments, the interface 330 can include a numerical (oralphanumerical display), the illumination source 302, the camera 304,combinations thereof, or the like. For example, instructions forproperly using the system 300 can be provided to the user via the GUI332. The GUI 332 can include at least one display or indicator forcommunicating information to the subject, such as the results of theauthentication process.

Although discussed herein as optimized ordering and authentication basedon a single iris, it should be understood that in some embodiments, thesystem 300 can perform the optimized ordering of the enrollment data 308and the authentication analysis on both irises of the subject. Forexample, rather than reordering of enrollment data 308 only for the leftiris, enrollment data 308 for both left and right irises can bereordered and matched to respective left and right iris probe images314. Authentication can further be independently performed on bothirises of the subject using the rearranged and optimized enrollment data308 for the respective left and right irises. Data associated withresults of authentication can be electronically stored in the database306 as authentication data 328. In some embodiments, the authenticationdata 328 can be used to improve operation of the system 300 through,e.g., machine learning, machine vision, or the like. In instances wherethe system 300 determines that a biometric match is not found based onthe short-range iris features, the system 300 can request an additionalprobe image 314 from the subject.

As noted above, broadband features in the texture of an iris of theprobe image 114 can focus on general features of the iris (as comparedto exact features located in the short-range analysis), and can bedetermined using any number of schemes including but not limited tosignal processing, computer vision methods, deep learning, machinelearning, machine vision, or the like. In the case of machine learning,the system 300 can train on a variety of irises to capture and producebroadband iris features, and such broadband iris features learned by anetwork can be used to characterize an iris. In the case of signalprocessing or computer vision, the system 300 can use an algorithm forcomputational vision methods with a filter to detect and select radialtransitions between a region of an iris with a broadband feature (e.g.,a collarette) and a region beyond the broadband feature. For example,filters can be used to extract short-range iris texture, filters can beused to extract the broadband iris texture, human-detected iris featurescan be used, machine-detected iris patterns can be used (e.g., doublering of dots, flames, or the like). In the case of deep learning, thenetwork can classify the coarse features if the network is first seededwith a number of exemplars. A loosely supervised deep learning networkcan therefore discover an optimal number of features. Such a network canfurther discern the level of each feature present in a probe iris andthe levels in each of the list of enrolled irises.

The act of reordering the enrollment data 308 into the optimized order334 can take into account that each enrollment iris includes encodedtherein its characteristics that are precomputed and built in to theenrollment data 308. As an example, when presented with a probe iris, alist of distances can be computed between the probe iris and each of theenrolled irises. The distances are can be the basis of ordering. In someembodiments, pointers can be generated by the system 300 that countsfrom the closest to the furthest iris. Such pointers can be assigned toeach enrolled iris. The matching engine executed by the processingdevice 322 can call each of the enrolled irises to be matched bycounting through the assigned pointers in order, thereby ordering theattempted matches from most likely to match through least likely tomatch.

A variety of methods for optimizing the order the enrollment images canbe used. In some embodiments, the enrollment images can be arrangedbased on the closest similarity between one or more broadband irisfeatures of the probe image and the broadband iris features of theenrollment images (e.g., a 1, 2, 3, etc. order of enrollment images with1 representing the closest broadband iris feature match, and decreasingin similarity). In some embodiments, Euclidian vector distancemeasurements relative to the probe can be used to determine thesimilarity between the broadband iris feature of the probe andenrollment images. In some embodiments, non-Euclidian vector distancemeasurements relative to the probe can be used to determine thesimilarity between the broadband iris feature of the probe andenrollment images. In some embodiments, after the enrollment images havebeen arranged based on decreasing similarity, only a percentage of thearranged enrollment images can be used for matching (e.g., top 50enrollment images, top 500 enrollment images, top 5% enrollment images,top 15% enrollment images, or the like).

In some embodiments, the enrollment images can be arranged by groupingthe enrollment images having similar broadband iris features (e.g.,group all enrollment images having crypts, group all enrollment imageshaving nodules, group all enrollment images having collarettes, or thelike). In some embodiments, the enrollment images can be arranged byfirst grouping enrollment images having the existence of the samebroadband iris feature (e.g., group all enrollment images havingcrypts), and next arranging the enrollment images within the selectedgroup based on the intensity of the broadband iris feature shared by thegroup (e.g., a 1, 2, 3, etc. order of enrollment images with 1representing the image having the greatest intensity of the broadbandfeature). In some embodiments, if the probe image includes only a singlebroadband iris feature, the arrangement can be performed only onenrollment images having the same broadband iris feature while theremaining enrollment images remain in random order.

In some embodiments, the broadband feature analysis can determine thedegree to which the features in the probe image 314 are present, and thecoarsely defined region where the feature is found, e.g., in one of twoannular rings, inner half of the iris, or outer half of the iris. Forexample, the system 300 can determine the presence of horizontal orvertical stripes in the inner half of the iris closest to the pupil. Thenumber of broadband iris features found in the texture of a probe iriscan be combined (by the processing device 322) into a feature vector. Insome embodiments, the feature vector can include a listing of featuresand their measured strength.

During the optimized ordering of the enrollment data 308, the measuredfeature vector of the probe iris for the broadband iris texture 318 canbe used to organize the list of enrolled irises against which the probeimage 314 will be compared to find matches. For example, feature vectorsfor each iris in the enrollment data 308 can be prepared by the system300 in advance of the biometric matching process. The ordering of thelist of enrolled irises can be on the basis of some or all of thebroadband iris texture 318 features of the probe iris feature vector. Insome embodiments, the compared feature vector of a probe image 314 andthose of the enrolled irises can create a set of distances between theprobe image 314 and each of the enrolled irises. In some embodiments,such distance can be defined by a Euclidean measure or any other means.

The distance measure can characterize the similarity of the featurevector of the probe image 314 to that of each enrolled iris in theenrollment data 308. The enrollment data 308 can be ordered from smallto large distance, e.g., with irises most resembling the probe iris interms of coarse or broadband features at the top of the list to thosenot resembling the probe iris at the bottom of the enrollment data 308list. In some embodiments, the list of enrollment irises can be orderedbased on at least one coarse or broadband textural feature that anenrolled iris shares in common with the probe iris. In such embodiments,the top of the list can include enrolled irises that share the commonfeature or features with the probe iris, while the enrolled irises thatdo not share the probe's features can be demoted to the bottom of theenrollment data 308 list.

As an example, N can represent the number of coarse or broadbandfeatures found in the iris (F₁, F₂, F_(N)), such as crypts, sunbursts,flames, or the like. A feature vector can include N features listing theamount or proportion of each of the N features found to be present inthe iris texture in question. In some embodiments, the amount orproportion of each broadband feature can be represented as a numericalvalue between 0 and 1, with 0 indicating a complete absence of thebroadband feature and 1 indicating the highest level of detection orpresence of the broadband feature. In some embodiments, differentnumerical value ranges can be used to represent the magnitude ofpresence of each broadband iris feature. For example, if N=3 for threebroadband features being analyzed, the feature vector of a first iriscan be represented as (0, 1, 0). Such feature vector can indicate thatfeature F₂ is present while features F₁ and F₃ are absent. A second iriswith a feature vector of (0.1, 0.8, 0.1) may be considered a match tothe first iris vector of (0, 1, 0) due to the smaller F₁ and F₃ valuesand close relationship of the F₂ values. Particularly, a match can befound based on the similarity in the texture of the probe and enrollmentirises.

However, a third iris with a feature vector of (0.4, 0, 0.6) may beconsidered a non-match to the first iris vector of (0, 1, 0) due to thedifference in values for the F₁ and F₃ features and the non-existent F₂feature. In some embodiments, the determination of match or non-matchcan be based on whether the magnitude of the feature is within apredetermined threshold range (e.g., within ±0.2). In some embodiments,comparison of two feature vectors to determine the degree to whichbroadband characteristics indicate a match can use a variety of methodsincluding but not limited to binary comparison of at least one feature,Euclidean distance, non-Euclidean distance, a dot product analysis, aweighted dot product analysis, weighted distance measurement,mathematical comparison of two feature vectors relative to a threshold,or the like.

In some embodiments, the broadband iris features discussed herein caninclude the measurement of the presence of “crypts” in an iris as aparticular broadband feature. In some embodiments, measurement of thepresence of crypts can be performed without considering the exactposition, size or relative orientation of the crypts. For example,rather than analyzing the probe and enrollment images for a specificquadrant or radial position of a broadband feature, the system cananalyze the images to determine if the broadband feature is detected inany location of the iris. By analyzing the images without consideringthe exact position, size or relative orientation of the broadbandfeature, the system is capable of detecting potential matches even ifthe orientation of the captured iris is different from the orientationof the enrollment images. The presence of crypts on their own canindicate a potential broadband iris feature match, and such enrollmentiris can be placed closer to the top of the optimized order 334. In someembodiments, the system 300 can consider the exact position, size and/orrelative orientation of the crypts. For example, the system 300 candetermine that the crypts are present only in the inner portion of theiris nearest the pupil in the enrollment image, while in a second iris(e.g., the probe image 314), similar crypts are found only in the otherportion of the iris near the sclera. Based on such determination, thesystem 300 can indicate that the two irises do not match and places theenrollment iris closer to the bottom of the optimized order 334.

Rather than focusing on the exact position and/or shape of a broadbandiris feature, the system 300 focuses on detecting the existence of thebroadband iris feature. Generally, broadband iris features form either aring of features surrounding the pupil (e.g., crypts) or features thatloosely encircle the iris (e.g., nodules). As an example, if the probeimage of the iris includes a ring of crypts, the system 300 can beconfigured to analyze the enrollment image to determine the existence ofa ring of crypts in the enrollment image. If the system 300 determinesthat a ring of crypts exists in the enrollment image, whether of thesame or different diameter and/or configuration as the crypts in theprobe image, the system 300 can output at least a low-confidence match.If a match is found to exist based on these broadband iris features, thesystem 300 can place the enrollment iris closer to the top of theoptimized order 334. The enrollment irises having the closest matchbased on broadband iris features can subsequently be biometricallyanalyzed based on the short-range iris features. The resulting searchand analysis for an authentic iris based on the short-range irisfeatures can take fewer match attempts than by pure chance, therebyspeeding the overall matching and authentication process.

Particularly, the probe iris can be biometrically analyzed and comparedwith (e.g., matched to) each of the enrolled irises in the orderdetermined by the previous steps. The optimized order 334 ensures thatan authentic match (e.g., a match score that exceeds a matchingthreshold) will be found earlier in the list than would be found by purechance. In some embodiments, for a list of N enrolled irises, onaverage, pure chance would match a probe to an authentic iris after N/2attempts that resulted in false matches.

If the system 300 determines that the broadband feature vector of theprobe image 314 and the broadband feature vector of the enrolled irismeet or exceed a predetermined level, referred to herein as a threshold,a positive match can be output and the enrolled iris can be placedcloser to the top or start of the optimized order 334. If the broadbandiris features of the probe image 314 and of the enrolled iris do notmeet the predetermined threshold, a non-match can be output and theenrolled iris can be placed closer to the bottom or end of the optimizedorder 334.

In some embodiments, a single broadband iris feature can be used for thebiometric analysis, and the presence or absence of such broadband irisfeature can be used reorder the enrollment data 308. In someembodiments, multiple broadband iris features can be combined in afeature vector of multiple dimensions to reorder the enrollment data308. In some embodiments, the binary presence or absence of an irisfeature can be used for the optimized order 334. In some embodiments, aEuclidean distance between the probe and enrolled iris can be used togenerate the optimized order 334. In some embodiments, a distance otherthan strictly Euclidean can be used. In some embodiments, only part ofthe enrollment data 308 can be reordered. For example, the neighboringirises (those with a sufficiently small distance to the probe iris) canbe pulled out of line and promoted to the front of the line, while theremaining enrollment irises can retain their original order.

In some embodiments, varying processing of characterizing the coarse orbroadband features of an iris can be implemented. In some embodiments,pattern recognition can use rationally designed filters to indicate thepresence of various types of iris texture as well as the physicallocation of patches of a particular type of texture. For example, onefilter can respond to vertical stripes while another filter can respondto horizontal stripes, and a third filter can respond to a checkerboardpattern. Filters can be used to search for the local or global presenceof a particular pattern. For example, iris A can be detected to havehorizontal stripes or iris B can be detected to have vertical stripes inthe inner half of the iris nearest the pupil. In some embodiments,machine learning and/or machine vision can be used to train on databasesof iris texture to find characteristics that distinguish one group ofirises from another Such patterns or textures can be extracted by themachine learning and/or machine vision operation of the system 300 togenerate the optimized order 334 of the enrolled data 308 prior toshort-range iris feature authentication.

In some embodiments, the system 300 can be used to reorder theenrollment data 308 and/or authenticate the subject based on analysis ofa single iris. In some embodiments, the system 300 can be used toreorder the enrollment data 308 and/or authenticate the subject based onanalysis of both the left and right irises. In such embodiments, atleast one camera 304 can produce probe images 314 of both the left andright irises of the subject, and the system 300 analyzes the probeimages 314 relative to respective left and right iris enrollment imagesof the subject. The system 300 can fuse the results increasing thechance of determining a subsequent match based on the short-range irisfeatures. For example, the system 300 can biometrically analyze thebroadband iris features for both the left and right enrolled irises togenerate the optimized order 334 for both the left and right enrollediris pairs. By finding that both the left and right enrolled irisesmatch the respective coarse or broadband features of the probe irises,and placing the enrolled irises closer to the top of the optimized order334, the system 300 ensures an efficient matching process based on theshort-range features.

As noted above, based on the distinguished textural features, theoptimized order of enrollment data can be generated. By generating theoptimized order of the enrollment data based on existence of or lack ofbroadband iris features (such as crypts) depending on the character ofthe probe iris, the number of matches needed to find an authentic matchcan be reduced to less than N/2 (less than the number in the case ofpure chance authentication).

As an example, the system 300 can include a database with N enrollmentimages arranged in random order (e.g., with no regard to iris texture).On average, approximately N/2 irises are examined by traditionalbiometric analysis systems before a match with an authentic probe isexpected. It is understood that for rejecting an impostor, the system300 would analyze all N irises. The example discussed herein thereforerefers to an instance of an authentic probe.

It can be assumed that some fraction of the total enrollment databaseexhibits a particular feature (e.g., Fuchs' Crypts), while the rest ofthe enrollment database does not. Experimentation was performed todetermine the average or expected number of attempted matches to findthe authentic match if the enrollment database is reordered bypositioning all enrollments with the particular broadband featuretogether first and demoting all enrollments without the particularbroadband feature to positions in the enrollment list below the selectedgroup. For example, all of the enrolled irises with crypts can begrouped at the top of the optimized order followed by enrolled iriseswithout crypts.

FIG. 15 is a diagrammatic representation of an N-membered enrollmentdatabase (e.g., enrollment data 308) used to determine the accelerationfor matching if optimized ordering is generated. Particularly, FIG. 15schematically illustrates the reordered enrollment database in terms ofa fraction a of enrolled irises with the particular feature and aprobability P of correctly identifying the feature in the probe iris.Thus, the probe iris can be incorrectly evaluated to have the broadbandfeature when the probe iris really does not or evaluated to not have thebroadband feature when it really does with a probability of (1−P).

If the broadband feature of the probe iris has been correctly evaluated,the authentic match can occur in the top group of the enrollmentdatabase after an average of aN/2 attempts. The probability of thisevent can be represented by P. However, if the broadband feature of theprobe has been incorrectly evaluated (probability, 1−P), then onaverage, αN/2+(1−α)N=N(1−α/2) match attempts would occur before anauthentic match. Weighting each outcome by its probability, the expectednumber of matches N_(exp) can be represented by Equation 1.

$\begin{matrix}{N_{\exp} = {{{P \cdot \alpha \cdot \frac{N}{2}} + {\left( {1 - P} \right) \cdot \left( {1 - \frac{\alpha}{2}} \right) \cdot N}} = {\frac{N}{2} \cdot \left( {\alpha + 1 - P} \right)}}} & (1)\end{matrix}$

Comparing the number of attempts to the number without reordering theenrollment database (N/2) provides an acceleration or speed-up factor Arepresented by Equation 2.

$\begin{matrix}{A = \frac{1}{\alpha + 1 - P}} & (2)\end{matrix}$

For example, using Fuchs' Crypts as the distinguishing broadbandfeature, α=0.4 and P=0.9, resulting in A=2. However, if α=0.2 and P=0.95(as may be the case with five evenly distributed groups, such asfingerprint classes and robust evaluation), A=4. As the size of theprioritized group shrinks and the probability of correctly assessing thefeature that distinguishes the group increases, A increases with A˜1/αwith high probability of correct feature assessment and A˜1/(1−P) as thefraction a becomes small.

In some embodiments, a different scheme of database ordering can beimplemented if the degree of each of several (e.g., N) features wereavailable. In such embodiments, each enrolled iris can reside in anN-dimensional space and the distance in such space (e.g., Euclideandistance) between a probe and an enrollment iris would determine thematching order.

FIG. 16 is a flowchart illustrating an exemplary process 400 ofimplementing the system 300. To begin, at step 402, the iris of thesubject is illuminated with at least one illumination source. At step404, at least one probe image of the iris of the subject can be capturedwith at least one camera. Each of the probe images includes irisbiometric data in the form of short and broadband iris textureinformation. At step 406, a processing device can receive as input theprobe image and generates an optimized order of enrollment irisbiometric data electronically stored in a database based on biometricanalysis of the probe broadband iris texture information relative toenrollment broadband iris texture information. The optimized orderincludes a listing of the enrollment iris biometric data ordered byclosest match to furthest match between the probe and enrollmentbroadband iris texture information. At step 408, the processing devicecan analyze the iris biometric data for biometric authenticity based onthe probe short-range iris texture information and enrollmentshort-range iris texture information starting with the closest matchbetween the probe and enrollment broadband texture information.

FIG. 17 is a block diagram of a computing device 500 in accordance withexemplary embodiments of the present disclosure. The computing device500 includes one or more non-transitory computer-readable media forstoring one or more computer-executable instructions or software forimplementing exemplary embodiments. The non-transitory computer-readablemedia may include, but are not limited to, one or more types of hardwarememory, non-transitory tangible media (for example, one or more magneticstorage disks, one or more optical disks, one or more flash drives), andthe like. For example, memory 506 included in the computing device 500may store computer-readable and computer-executable instructions orsoftware for implementing exemplary embodiments of the presentdisclosure (e.g., instructions for operating the illumination sources,instructions for operating the cameras, instructions for operating theprocessing device, instructions for operating the communicationinterface, instructions for operating the user interface, instructionsfor operating the central computing system, combinations thereof, or thelike). The computing device 500 also includes configurable and/orprogrammable processor 502 and associated core 504, and optionally, oneor more additional configurable and/or programmable processor(s) 502′and associated core(s) 504′ (for example, in the case of computersystems having multiple processors/cores), for executingcomputer-readable and computer-executable instructions or softwarestored in the memory 506 and other programs for controlling systemhardware. Processor 502 and processor(s) 502′ may each be a single coreprocessor or multiple core (504 and 504′) processor.

Virtualization may be employed in the computing device 500 so thatinfrastructure and resources in the computing device 500 may be shareddynamically. A virtual machine 514 may be provided to handle a processrunning on multiple processors so that the process appears to be usingonly one computing resource rather than multiple computing resources.Multiple virtual machines may also be used with one processor. Memory506 may include a computer system memory or random access memory, suchas DRAM, SRAM, EDO RAM, and the like. Memory 506 may include other typesof memory as well, or combinations thereof.

A user may interact with the computing device 500 through a visualdisplay device 518 (e.g., a personal computer, a mobile smart device, orthe like), such as a computer monitor, which may display at least oneuser interface 520 (e.g., a graphical user interface) that may beprovided in accordance with exemplary embodiments. The computing device500 may include other I/O devices for receiving input from a user, forexample, a camera, a keyboard, a fingerprint scanner, microphone, or anysuitable multi-point touch interface 508, a pointing device 510 (e.g., amouse). The keyboard 508 and the pointing device 510 may be coupled tothe visual display device 518. The computing device 500 may includeother suitable conventional I/O peripherals.

The computing device 500 may also include at least one storage device524, such as a hard-drive, CD-ROM, eMMC (MultiMediaCard), SD (securedigital) card, flash drive, non-volatile storage media, or othercomputer readable media, for storing data and computer-readableinstructions and/or software that implement exemplary embodiments of thebiometric analysis systems described herein. Exemplary storage device524 may also store at least one database 526 for storing any suitableinformation required to implement exemplary embodiments. For example,exemplary storage device 524 can store at least one database 526 forstoring information, such as data relating to probe images, enrollmentdata, authentication data, combinations thereof, or the like, andcomputer-readable instructions and/or software that implement exemplaryembodiments described herein. The databases 526 may be updated bymanually or automatically at any suitable time to add, delete, and/orupdate one or more items in the databases.

The computing device 500 can include a network interface 512 configuredto interface via at least one network device 522 with one or morenetworks, for example, a Local Area Network (LAN), a Wide Area Network(WAN) or the Internet through a variety of connections including, butnot limited to, standard telephone lines, LAN or WAN links (for example,802.11, T1, T3, 56 kb, X.25), broadband connections (for example, ISDN,Frame Relay, ATM), wireless connections, controller area network (CAN),or some combination of any or all of the above. The network interface512 may include a built-in network adapter, a network interface card, aPCMCIA network card, Pa Cl/PCIe network adapter, an SD adapter, aBluetooth adapter, a card bus network adapter, a wireless networkadapter, a USB network adapter, a modem or any other device suitable forinterfacing the computing device 500 to any type of network capable ofcommunication and performing the operations described herein. Moreover,the computing device 500 may be any computer system, such as aworkstation, desktop computer, server, laptop, handheld computer, tabletcomputer (e.g., the tablet computer), mobile computing or communicationdevice (e.g., the smart phone communication device), an embeddedcomputing platform, or other form of computing or telecommunicationsdevice that is capable of communication and that has sufficientprocessor power and memory capacity to perform the operations describedherein.

The computing device 500 may run any operating system 516, such as anyof the versions of the Microsoft® Windows® operating systems, thedifferent releases of the Unix and Linux operating systems, any versionof the MacOS® for Macintosh computers, any embedded operating system,any real-time operating system, any open source operating system, anyproprietary operating system, or any other operating system capable ofrunning on the computing device and performing the operations describedherein. In exemplary embodiments, the operating system 516 may be run innative mode or emulated mode. In an exemplary embodiment, the operatingsystem 516 may be run on one or more cloud machine instances.

FIG. 18 is a block diagram of an exemplary biometric analysis systemenvironment 600 in accordance with exemplary embodiments of the presentdisclosure. The environment 600 can include servers 602, 604 configuredto be in communication with at least one illumination source 606, atleast one camera 608, at least one processing device 610, a userinterface 612, and a central computing system 614 via a communicationplatform 620, which can be any network over which information can betransmitted between devices communicatively coupled to the network. Forexample, the communication platform 620 can be the Internet, Intranet,virtual private network (VPN), wide area network (WAN), local areanetwork (LAN), and the like. In some embodiments, the communicationplatform 620 can be part of a cloud environment.

The environment 600 can include repositories or databases 616, 618,which can be in communication with the servers 602, 604, as well as theat least one illumination source 606, at least one camera 608, at leastone processing device 610, user interface 612, and central computingsystem 614, via the communications platform 620.

In exemplary embodiments, the servers 602, 604, the at least oneillumination source 606, at least one camera 608, at least oneprocessing device 610, user interface 612, and central computing system614 can be implemented as computing devices (e.g., computing device500). Those skilled in the art will recognize that the databases 616,618 can be incorporated into at least one of the servers 602, 604. Insome embodiments, the databases 616, 618 can store data relating toprobe images, enrollment data, authentication data, combinationsthereof, or the like, and such data can be distributed over multipledatabases 616, 618.

The exemplary systems disclosed herein provide for accurate and/orefficient biometric authentication. Although the present disclosure hassometimes explained the biometric analysis matching accuracy enhancingsystem and the biometric analysis matching efficiency enhancing systemusing separate explanations and/or as separate systems, it should beunderstood by a person of ordinary skill in the art that one or morecomponents of each of systems can be presented in combination, such thata system is provide for enhancing both accuracy and efficiency. In someembodiments, the exemplary systems can function together as a singlebiometric analysis system, with each system providing a level ofaccuracy and/or efficiency for biometric authentication.

While exemplary embodiments have been described herein, it is expresslynoted that these embodiments should not be construed as limiting, butrather that additions and modifications to what is expressly describedherein also are included within the scope of the invention. Moreover, itis to be understood that the features of the various embodimentsdescribed herein are not mutually exclusive and can exist in variouscombinations and permutations, even if such combinations or permutationsare not made express herein, without departing from the spirit and scopeof the invention.

1. A system at least for enhancing biometric analysis matchingefficiency, comprising: at least one camera configured to capture atleast one probe image of an iris of a subject, the at least one probeimage having iris biometric data associated with the iris of thesubject, the iris biometric data including probe short-range iristexture information and probe broadband iris texture information; adatabase electronically storing enrollment iris biometric data includingenrollment short-range iris texture information and enrollment broadbandiris texture information; and a processing device in communication withthe at least one camera and the database, the processing deviceconfigured to: (i) generate an optimized order of the enrollment irisbiometric data based on biometric analysis of the probe broadband iristexture information relative to the enrollment broadband iris textureinformation, the optimized order including a listing of the enrollmentiris biometric data ordered by closest match to furthest match betweenthe probe and enrollment broadband iris texture information; and (ii)analyze the iris biometric data for biometric authenticity based on theprobe and enrollment short-range iris texture information starting withthe closest match between the probe and enrollment broadband iristexture information.
 2. The system of claim 1, comprising at least oneillumination source configured to illuminate an iris of a subject. 3.The system of claim 2, wherein the processing device is in communicationwith the at least one illumination source.
 4. The system of claim 2,wherein the at least one illumination source is configured to illuminatethe iris of the subject with near infrared light.
 5. The system of claim2, wherein the at least one camera is configured to capture the at leastone probe image of the iris of the subject during illumination of thesubject with the at least one illumination source.
 6. The system ofclaim 1, wherein biometric analysis of the probe broadband iris textureinformation relative to the enrollment broadband iris textureinformation comprises generating a feature vector for each of the probeand enrollment broadband iris texture information.
 7. The system ofclaim 6, wherein biometric analysis of the probe broadband iris textureinformation relative to the enrollment broadband iris textureinformation comprises generating a set of distances between the featurevector of the probe broadband iris texture information and the featurevector of each of the enrollment broadband iris texture information. 8.The system of claim 7, wherein the set of distances between the featurevectors is defined by a Euclidian distance determination.
 9. The systemof claim 7, wherein the set of distances between the feature vectorscharacterizes a similarity between the feature vector of the probe andenrollment broadband iris texture information.
 10. The system of claim7, wherein a small distance between the feature vectors of the probe andenrollment broadband iris texture information corresponds with a closematch between the probe and enrollment broadband iris textureinformation, and a large distance between the feature vectors of theprobe and enrollment broadband iris texture information corresponds witha far match between the probe and enrollment broadband iris textureinformation.
 11. The system of claim 1, wherein analyzing the irisbiometric data for biometric authenticity comprises comparing the probeshort-range iris texture information to the enrollment short-range iristexture information.
 12. The system of claim 1, wherein the at least oneprobe image includes iris biometric data associated with left and rightirises of the subject.
 13. The system of claim 12, wherein theprocessing device is configured to generate the optimized order of theenrollment iris biometric data for both the left and right irises of thesubject.
 14. A method at least for enhancing biometric analysis matchingefficiency, comprising: capturing at least one probe image of an iris ofa subject with at least one camera, the at least one probe image havingiris biometric data associated with the iris of the subject, the irisbiometric data including probe short-range iris texture information andprobe broadband iris texture information; generating, via a processingdevice, an optimized order of enrollment iris biometric dataelectronically stored in a database based on biometric analysis of theprobe broadband iris texture information relative to enrollmentbroadband iris texture information, the optimized order including alisting of the enrollment iris biometric data ordered by closest matchto furthest match between the probe and enrollment broadband iristexture information; and analyzing, via the processing device, the irisbiometric data for biometric authenticity based on the probe short-rangeiris texture information and enrollment short-range iris textureinformation starting with the closest match between the probe andenrollment broadband texture information.
 15. The method of claim 14,comprising illuminating the iris of the subject with near infrared lightfrom at least one illumination source.
 16. The method of claim 14,comprising: generating a feature vector for each of the probe andenrollment broadband iris texture information; and generating a set ofdistances between the feature vector of the probe broadband iris textureinformation and the feature vector of each of the enrollment broadbandiris texture information.
 17. The method of claim 16, wherein a smalldistance between the feature vectors of the probe and enrollmentbroadband iris texture information corresponds with a close matchbetween the probe and enrollment broadband iris texture information, anda large distance between the feature vectors of the probe and enrollmentbroadband iris texture information corresponds with a far match betweenthe probe and enrollment broadband iris texture information.
 18. Anon-transitory computer-readable medium storing instructions at leastfor enhancing biometric analysis matching efficiency that are executableby a processing device, wherein execution of the instructions by theprocessing device causes the processing device to: capture at least oneprobe image of an iris of a subject with at least one camera, the atleast one probe image having iris biometric data associated with theiris of the subject, the iris biometric data including probe short-rangeiris texture information and probe broadband iris texture information;generate, via a processing device, an optimized order of enrollment irisbiometric data electronically stored in a database based on biometricanalysis of the probe broadband iris texture information relative toenrollment broadband iris texture information, the optimized orderincluding a listing of the enrollment iris biometric data ordered byclosest match to furthest match between the probe and enrollmentbroadband iris texture information; and analyze, via the processingdevice, the iris biometric data for biometric authenticity based on theprobe short-range iris texture information and enrollment short-rangeiris texture information starting with the closest match between theprobe and enrollment broadband texture information.
 19. An enhanced datastorage efficiency system for a computer memory, comprising: means forcapturing at least one probe image of an iris of a subject, the at leastone probe image having iris biometric data associated with the iris ofthe subject, the iris biometric data including probe short-range iristexture information and probe broadband iris texture information; meansfor generating an optimized order of enrollment iris biometric dataelectronically stored in a database based on biometric analysis of theprobe broadband iris texture information relative to enrollmentbroadband iris texture information, the optimized order including alisting of the enrollment iris biometric data ordered by closest matchto furthest match between the probe and enrollment broadband iristexture information; and means for analyzing the iris biometric data forbiometric authenticity based on the probe short-range iris textureinformation and enrollment short-range iris texture information startingwith the closest match between the probe and enrollment broadbandtexture information.
 20. A system at least for enhancing biometricanalysis matching efficiency and enhancing biometric analysis matchingaccuracy, comprising: at least one camera configured to capture at leastone probe image of an iris of a subject, the at least one probe imagehaving iris biometric data associated with the iris of the subject, theiris biometric data including probe short-range iris texture informationand probe broadband iris texture information; a database electronicallystoring enrollment iris biometric data including enrollment short-rangeiris texture information and enrollment broadband iris textureinformation; and a processing device in communication with the at leastone camera and the database, the processing device configured to: (i)generate an optimized order of the enrollment iris biometric data basedon biometric analysis of the probe broadband iris texture informationrelative to the enrollment broadband iris texture information, theoptimized order including a listing of the enrollment iris biometricdata ordered by closest match to furthest match between the probe andenrollment broadband iris texture information; (ii) analyze the irisbiometric data for biometric authenticity based on the probe andenrollment short-range iris texture information starting with theclosest match between the probe and enrollment broadband iris textureinformation, said analysis of the iris biometric authenticity comprisinganalyzing the probe short-range iris texture information of the at leastone probe image for iris biometric authenticity and analyzing the probebroadband iris texture information of the at least one probe image foriris biometric authenticity, and, based on the biometric authenticity ofthe probe short-range iris texture information and the probe broadbandiris texture information, determine the biometric authenticity of thesubject.
 21. The system of claim 20, comprising at least oneillumination source configured to illuminate an iris of a subject,wherein the processing device is in communication with the at least oneillumination source.
 22. The system of claim 21, wherein the at leastone illumination source is configured to illuminate the iris of thesubject with near infrared light.
 23. The system of claim 21, whereinthe at least one camera is configured to capture the at least one probeimage of the iris of the subject during illumination of the subject withthe at least one illumination source.
 24. The system of claim 20,wherein: analyzing the probe short-range iris texture information forbiometric authenticity comprises comparing the probe short-range iristexture information to enrollment short-range iris texture information;and analyzing the probe broadband iris texture information for biometricauthenticity comprises comparing the probe broadband iris textureinformation to enrollment broadband iris texture information.
 25. Thesystem of claim 20, wherein the processing device is configured to applythe biometric authenticity determination based on the probe broadbandiris texture information as a final deciding factor in the biometricauthenticity of the subject.
 26. The system of claim 20, wherein: the atleast one probe image includes iris biometric data associated with leftand right irises of the subject; and the processing device is configuredto analyze the probe short-range and broadband iris texture informationfor both the left and right irises of the subject.
 27. The system ofclaim 20, wherein: the processing device is configured to generate ashort-range score corresponding with a degree to which the irisbiometric authenticity is found for the probe short-range iris textureinformation; if the generated score for the iris biometric authenticityof the probe short-range iris texture information is better than ashort-range threshold value, the processing device is configured toanalyze the probe broadband iris texture information of the at least oneprobe image for the iris biometric authenticity; the processing deviceis configured to generate a broadband score corresponding with a degreeto which the iris biometric authenticity is found for the probebroadband iris texture information; and if the generated broadband scoreis better than a broadband threshold value, positive biometricauthenticity of the subject is determined.
 28. The system of claim 20,wherein: biometric analysis of the probe broadband iris textureinformation relative to the enrollment broadband iris textureinformation comprises generating a feature vector for each of the probeand enrollment broadband iris texture information; and biometricanalysis of the probe broadband iris texture information relative to theenrollment broadband iris texture information comprises generating a setof distances between the feature vector of the probe broadband iristexture information and the feature vector of each of the enrollmentbroadband iris texture information.
 29. The system of claim 28, wherein:the set of distances between the feature vectors is defined by aEuclidian distance determination; the set of distances between thefeature vectors characterizes a similarity between the feature vector ofthe probe and enrollment broadband iris texture information; or a smalldistance between the feature vectors of the probe and enrollmentbroadband iris texture information corresponds with a close matchbetween the probe and enrollment broadband iris texture information, anda large distance between the feature vectors of the probe and enrollmentbroadband iris texture information corresponds with a far match betweenthe probe and enrollment broadband iris texture information.
 30. Thesystem of claim 20, wherein analyzing the iris biometric data forbiometric authenticity comprises comparing the probe short-range iristexture information to the enrollment short-range iris textureinformation.
 31. The system of claim 20, wherein: the at least one probeimage includes iris biometric data associated with left and right irisesof the subject; and the processing device is configured to generate theoptimized order of the enrollment iris biometric data for both the leftand right irises of the subject.